{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "学习目标\n",
    "- 通过电影数据联系，熟悉pandas基本数据处理思路"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "99258d822cd42807"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1 需求\n",
    "现在我们有一组从2006年到2016年1000部最流行的电影数据\n",
    "- 问题1：我们想知道这些电影数据中评分的平均分，导演的人数等信息，我们应该怎么获取？\n",
    "- 问题2：对于这一组电影数据，如果我们想rating，runtime的分布情况，应该如何呈现数据？\n",
    "- 问题3：对于这一组电影数据，如果我们希望统计电影分类(genre)的情况，应该如何处理数据？\n",
    "# 2 实现\n",
    "首先获取导入包，获取数据"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "900baf365db0c39d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "     Rank                    Title                     Genre  \\\n0       1  Guardians of the Galaxy   Action,Adventure,Sci-Fi   \n1       2               Prometheus  Adventure,Mystery,Sci-Fi   \n2       3                    Split           Horror,Thriller   \n3       4                     Sing   Animation,Comedy,Family   \n4       5            Suicide Squad  Action,Adventure,Fantasy   \n..    ...                      ...                       ...   \n995   996     Secret in Their Eyes       Crime,Drama,Mystery   \n996   997          Hostel: Part II                    Horror   \n997   998   Step Up 2: The Streets       Drama,Music,Romance   \n998   999             Search Party          Adventure,Comedy   \n999  1000               Nine Lives     Comedy,Family,Fantasy   \n\n                                           Description              Director  \\\n0    A group of intergalactic criminals are forced ...            James Gunn   \n1    Following clues to the origin of mankind, a te...          Ridley Scott   \n2    Three girls are kidnapped by a man with a diag...    M. Night Shyamalan   \n3    In a city of humanoid animals, a hustling thea...  Christophe Lourdelet   \n4    A secret government agency recruits some of th...            David Ayer   \n..                                                 ...                   ...   \n995  A tight-knit team of rising investigators, alo...             Billy Ray   \n996  Three American college students studying abroa...              Eli Roth   \n997  Romantic sparks occur between two dance studen...            Jon M. Chu   \n998  A pair of friends embark on a mission to reuni...        Scot Armstrong   \n999  A stuffy businessman finds himself trapped ins...      Barry Sonnenfeld   \n\n                                                Actors  Year  \\\n0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014   \n1    Noomi Rapace, Logan Marshall-Green, Michael Fa...  2012   \n2    James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016   \n3    Matthew McConaughey,Reese Witherspoon, Seth Ma...  2016   \n4    Will Smith, Jared Leto, Margot Robbie, Viola D...  2016   \n..                                                 ...   ...   \n995  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...  2015   \n996  Lauren German, Heather Matarazzo, Bijou Philli...  2007   \n997  Robert Hoffman, Briana Evigan, Cassie Ventura,...  2008   \n998  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014   \n999  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016   \n\n     Runtime (Minutes)  Rating   Votes  Revenue (Millions)  Metascore  \n0                  121     8.1  757074              333.13       76.0  \n1                  124     7.0  485820              126.46       65.0  \n2                  117     7.3  157606              138.12       62.0  \n3                  108     7.2   60545              270.32       59.0  \n4                  123     6.2  393727              325.02       40.0  \n..                 ...     ...     ...                 ...        ...  \n995                111     6.2   27585                 NaN       45.0  \n996                 94     5.5   73152               17.54       46.0  \n997                 98     6.2   70699               58.01       50.0  \n998                 93     5.6    4881                 NaN       22.0  \n999                 87     5.3   12435               19.64       11.0  \n\n[1000 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Rank</th>\n      <th>Title</th>\n      <th>Genre</th>\n      <th>Description</th>\n      <th>Director</th>\n      <th>Actors</th>\n      <th>Year</th>\n      <th>Runtime (Minutes)</th>\n      <th>Rating</th>\n      <th>Votes</th>\n      <th>Revenue (Millions)</th>\n      <th>Metascore</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Guardians of the Galaxy</td>\n      <td>Action,Adventure,Sci-Fi</td>\n      <td>A group of intergalactic criminals are forced ...</td>\n      <td>James Gunn</td>\n      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n      <td>2014</td>\n      <td>121</td>\n      <td>8.1</td>\n      <td>757074</td>\n      <td>333.13</td>\n      <td>76.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Prometheus</td>\n      <td>Adventure,Mystery,Sci-Fi</td>\n      <td>Following clues to the origin of mankind, a te...</td>\n      <td>Ridley Scott</td>\n      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n      <td>2012</td>\n      <td>124</td>\n      <td>7.0</td>\n      <td>485820</td>\n      <td>126.46</td>\n      <td>65.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Split</td>\n      <td>Horror,Thriller</td>\n      <td>Three girls are kidnapped by a man with a diag...</td>\n      <td>M. Night Shyamalan</td>\n      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n      <td>2016</td>\n      <td>117</td>\n      <td>7.3</td>\n      <td>157606</td>\n      <td>138.12</td>\n      <td>62.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Sing</td>\n      <td>Animation,Comedy,Family</td>\n      <td>In a city of humanoid animals, a hustling thea...</td>\n      <td>Christophe Lourdelet</td>\n      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n      <td>2016</td>\n      <td>108</td>\n      <td>7.2</td>\n      <td>60545</td>\n      <td>270.32</td>\n      <td>59.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Suicide Squad</td>\n      <td>Action,Adventure,Fantasy</td>\n      <td>A secret government agency recruits some of th...</td>\n      <td>David Ayer</td>\n      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n      <td>2016</td>\n      <td>123</td>\n      <td>6.2</td>\n      <td>393727</td>\n      <td>325.02</td>\n      <td>40.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>995</th>\n      <td>996</td>\n      <td>Secret in Their Eyes</td>\n      <td>Crime,Drama,Mystery</td>\n      <td>A tight-knit team of rising investigators, alo...</td>\n      <td>Billy Ray</td>\n      <td>Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...</td>\n      <td>2015</td>\n      <td>111</td>\n      <td>6.2</td>\n      <td>27585</td>\n      <td>NaN</td>\n      <td>45.0</td>\n    </tr>\n    <tr>\n      <th>996</th>\n      <td>997</td>\n      <td>Hostel: Part II</td>\n      <td>Horror</td>\n      <td>Three American college students studying abroa...</td>\n      <td>Eli Roth</td>\n      <td>Lauren German, Heather Matarazzo, Bijou Philli...</td>\n      <td>2007</td>\n      <td>94</td>\n      <td>5.5</td>\n      <td>73152</td>\n      <td>17.54</td>\n      <td>46.0</td>\n    </tr>\n    <tr>\n      <th>997</th>\n      <td>998</td>\n      <td>Step Up 2: The Streets</td>\n      <td>Drama,Music,Romance</td>\n      <td>Romantic sparks occur between two dance studen...</td>\n      <td>Jon M. Chu</td>\n      <td>Robert Hoffman, Briana Evigan, Cassie Ventura,...</td>\n      <td>2008</td>\n      <td>98</td>\n      <td>6.2</td>\n      <td>70699</td>\n      <td>58.01</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>998</th>\n      <td>999</td>\n      <td>Search Party</td>\n      <td>Adventure,Comedy</td>\n      <td>A pair of friends embark on a mission to reuni...</td>\n      <td>Scot Armstrong</td>\n      <td>Adam Pally, T.J. Miller, Thomas Middleditch,Sh...</td>\n      <td>2014</td>\n      <td>93</td>\n      <td>5.6</td>\n      <td>4881</td>\n      <td>NaN</td>\n      <td>22.0</td>\n    </tr>\n    <tr>\n      <th>999</th>\n      <td>1000</td>\n      <td>Nine Lives</td>\n      <td>Comedy,Family,Fantasy</td>\n      <td>A stuffy businessman finds himself trapped ins...</td>\n      <td>Barry Sonnenfeld</td>\n      <td>Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...</td>\n      <td>2016</td>\n      <td>87</td>\n      <td>5.3</td>\n      <td>12435</td>\n      <td>19.64</td>\n      <td>11.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas  as pd \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "#文件的路径\n",
    "path = \"IMDB-Movie-Data.csv\"\n",
    "#读取文件\n",
    "df = pd.read_csv(path)\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-23T02:50:06.391269400Z",
     "start_time": "2024-02-23T02:50:06.372380200Z"
    }
   },
   "id": "e4ac65c15dad9f05",
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2.1 问题一解决方案\n",
    "我们想知道这些电影数据中评分的平均分，导演的人数等信息，我们应该怎么获取？\n",
    "- 得出评分的平均分\n",
    "使用mean函数"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "98781675646b5254"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "6.723199999999999"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Rating\"].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-23T02:50:06.418054500Z",
     "start_time": "2024-02-23T02:50:06.394235500Z"
    }
   },
   "id": "304ed46ba3d735e9",
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 得出导演人数信息\n",
    "求出唯一值，然后进行形状获取"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c307847076fde9ac"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "644"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 导演的人数\n",
    "# df[\"Director\"].unique().shape[0]\n",
    "np.unique(df[\"Director\"]).shape[0]\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-23T02:50:06.442054400Z",
     "start_time": "2024-02-23T02:50:06.398597700Z"
    }
   },
   "id": "2cf03bb930b725d7",
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2.2 问题二解决方案\n",
    "对于这一组电影数据，如果我们想Rating，Runtime (Minutes)的分布情况，应该如何呈现数据？\n",
    "- 直接呈现，以直方图的形式\n",
    "选择分数列数据，进行plot"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "9ce04557a832452d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: ylabel='Frequency'>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x800 with 1 Axes>",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Rating\"].plot(kind='hist',figsize=(20,8))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-23T02:50:06.526186900Z",
     "start_time": "2024-02-23T02:50:06.407054600Z"
    }
   },
   "id": "d1bc0e33d3d01fdc",
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "source": [
    "- Rating进行分布展示\n",
    "进行绘制直方图"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3e0ac3ad245de548"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "plt.hist(df[\"Rating\"].values,bins=20)\n",
    "# 求出最大最小值\n",
    "max_ = df[\"Rating\"].max()\n",
    "min_ = df[\"Rating\"].min()\n",
    "\n",
    "# 生成刻度列表\n",
    "t1 = np.linspace(min_,max_,num=21)\n",
    "\n",
    "# [ 1.9    2.255  2.61   2.965  3.32   3.675  4.03   4.385  4.74   5.095  5.45   5.805  6.16   6.515  6.87   7.225  7.58   7.935  8.29   8.645  9.   ]\n",
    "\n",
    "# 修改刻度\n",
    "plt.xticks(t1)\n",
    "\n",
    "# 添加网格\n",
    "plt.grid()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-23T02:50:33.477656900Z",
     "start_time": "2024-02-23T02:50:33.372213200Z"
    }
   },
   "id": "93484c5be0a62f19",
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "source": [
    "- Runtime (Minutes)进行分布展示\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8974ce3b16011279"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "plt.hist(df[\"Runtime (Minutes)\"].values,bins=20)\n",
    "# 求出最大最小值\n",
    "max_ = df[\"Runtime (Minutes)\"].max()\n",
    "min_ = df[\"Runtime (Minutes)\"].min()\n",
    "\n",
    "# # 生成刻度列表\n",
    "t1 = np.linspace(min_,max_,num=21)\n",
    "\n",
    "# 修改刻度\n",
    "plt.xticks(np.linspace(min_,max_,num=21))\n",
    "\n",
    "# 添加网格\n",
    "plt.grid()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-23T02:51:07.107583Z",
     "start_time": "2024-02-23T02:51:06.964277100Z"
    }
   },
   "id": "e6899832d84fe7ed",
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2.3 问题三解决方案\n",
    "对于这一组电影数据，如果我们希望统计电影分类(genre)的情况，应该如何处理数据？\n",
    "- 思路\n",
    "    - 1、创建一个全为0的dataframe，列索引置为电影的分类，temp_df\n",
    "    - 2、遍历每一部电影，temp_df中把分类出现的列的值置为1\n",
    "    - 3、求和"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "77f59c11db956bf1"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Musical        5.0\n",
      "Western        7.0\n",
      "War           13.0\n",
      "Music         16.0\n",
      "Sport         18.0\n",
      "History       29.0\n",
      "Animation     49.0\n",
      "Family        51.0\n",
      "Biography     81.0\n",
      "Fantasy      101.0\n",
      "Mystery      106.0\n",
      "Horror       119.0\n",
      "Sci-Fi       120.0\n",
      "Romance      141.0\n",
      "Crime        150.0\n",
      "Thriller     195.0\n",
      "Adventure    259.0\n",
      "Comedy       279.0\n",
      "Action       303.0\n",
      "Drama        513.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x800 with 1 Axes>",
      "image/png": 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cvPLKK5UZp6ecckp+9atfrbBaW7Itttgir732Wm655ZYceOCBjTrn9ttvz4ABA9KjR4+89NJLK7jClmXB8+61117Lq6++WpkNueWWWy5zBYs1eWli700u24pYYrIlv2ckgFkFPPHEE9lpp51SKpWy55575uc//3nmz5+fz33uc5UL9r3//vsZM2ZMLr/88vzlL3/JzjvvnBtvvDEbbbRRtctfqdq3b585c+bkrrvuyj777FPtclYro0ePzvDhw3P99ddn6tSpST7+5VZTU5O99torgwcPzoEHHrjGvOFUW1uburq6PPXUU/XWX2/ozfAFU75ra2sza9aslVxxyzBy5MjlOn9NXp6hS5cu+fe//50HH3yw3rTahp5zTz/9dD73uc+lTZs2mT179souuUX40Y9+lHPOOSddunTJmDFjsummmyZpuG8LlhHZYYcd8vjjj1ejbFZRZ511Vs4///zstddeueaaawQFy+Gpp57KsGHDct111+WDDz5I8vHrjlKplC996UsZPHhwBg4c6GL0ScaOHVt5Y/z9999P8p9e7brrrhk8eHAOPvjgtG3btsqVtjwfffRR/vKXv2T48OG59957M2/evMobvWuvvXYOO+ywDBo0KDvuuGOVK2059GzJFgQwi/5vwPJ5++2389vf/jYjRozIiy++mOQ/wV/Pnj1z7LHH5qijjsonPvGJapa5UjRnidgl2WKLLfLYY48t89onq6t27dpl7ty5GTNmTPr06dOoc8aOHZu+ffuukR+kXHjZ08Y+9yxN7L3Jxlje94aWpqW+ZySAWQUcc8wx+d3vfpfNNtss48ePT+vWrfP8889XPr276C+0yy+/PF//+tfzmc98Jk888cQa9Y/phhtumPfee88L3xVo9uzZ+fOf/5wzzjij8on6BX+Q119//Rx77LE59dRTs/HGG1ezzBVuwXOtKTNgFlw3ZpNNNskbb7yxsktmFde2bdt89NFHefLJJ/O5z32ucn9Dz7kFAf6avM7z9ttvn3/84x/5yU9+ku9+97uV+xvq20MPPZQvfelLWX/99SvXxFpd1dTUpKamJv/85z+XuH74vHnz8uabbyZJJbxiyX70ox8l+fh3/bPPPptWrVqlX79+jb7G1Zp0zb6mmDNnTm655ZYMHz48DzzwQObPn1953bHOOuvka1/7Wo499thGv4myOps7d25uu+22/OY3v8m9995br1edOnXK4YcfnkGDBmWHHXaocqUt0//93/9V3ugdP358kv+8xu3du3eOPfbY/M///E823HDDapbZoujZf/Tt2zdPP/10/va3vy02U4FiPPHEExk2bFhuuOGGTJs2LcnHz7dWrVpln332qXwgcEV8qrol2G233epdd2nkyJEplUrp27dvgzNgSqVS2rZtm0984hP54he/mMMPP3yZM2ZWZx07dsysWbPyyCOPNDooXvA/Vdu2bTNz5swVXGHLsujzrqkefPDBAqtZdXhvksVUaekzmmDLLbcs19TUlC+88MLKfc8991xlvbwlWbAO+cLnrAm+9KUvlWtqasq33nprtUtZLU2cOLF8zjnn1LsW0cLXU1h4zfZf/epX1S53herXr1+5pqamfN5559W7v6G1eI888shyqVQqH3DAASurTFYjXbt2LdfU1JRvuummevc39JwbNmxYuVQqlXv27Lmyymxx1llnnXJNTU35kUceqXd/Q317+umny6VSqdymTZuVVWbVLGv98AWvN1ryerotxaLrGDd1XWOW7cEHHyx/4hOfWGKPv/CFL5Rvu+22apfYYrz55pvln/zkJ+VevXotdi2F7bbbrnzhhRe6TkUD7rnnnspzbeHetWnTpnzYYYfVuxYbH1vTe/azn/2sXCqVyt/85jerXcpq74477ljq34JNNtmk/Otf/7pcV1dX7TJXuOZcA4ZyeYsttijX1NSUr7766kafc/XVV5dLpVJ50003XYGVsTrx3iSLWj0/GrCa+b//+78kybbbblu5b+FPdXz00UeLnfM///M/KZfLuf7661d8gS3ICSeckHK5nN///vfVLmW1MXv27PzhD3/IHnvskR49euRHP/pRJk6cmHK5nC233DIXXHBBJk+enHvvvTeHHXZYWrVqlVmzZuW0007LH/7wh2qXv8Lsv//+KZfLGTp0aKOWdvr73/+e6667LqVSKQcccMBKqJDVzYJZL6NGjWr0Ob/73e9SKpWy0047raiyWrwFP59NWR5xwdICa+o69ktSNmG6UcrlcuW26NfLurFks2bNyu9+97vsvvvu2XPPPfP2229XetarV6+0bds25XI5o0ePzsCBA/OVr3xljV1ycWFdu3bNqaeemu985zvZeOON6y0f8vzzz+fb3/52unXrlm9961uVpWX5+G/soEGD8tWvfrXyXEs+vu5EuVzORx99lBtvvDGf//znc8opp6xR19pcGj372De+8Y185jOfyeWXX56///3v1S5ntfP666/nRz/6UXr06JEDDzyw8lyrqanJ3nvvnU022STlcjmTJ0/ON7/5zey4447597//Xe2yV6ijjz46Rx99dNZdd91ql7JK+eIXv5hyuZyrr7660edceeWVKZVKa9zSijSf9yZZzEoIeVhOtbW15ZqamvLTTz9due/111+vfOLhzTffXOycp556qlwqlcpdunRZmaW2CEceeWS5pqamfP7551e7lFXao48+Wj7uuOPKnTt3rvfpovbt25ePOuqo8qhRo5Z43ssvv1z+7Gc/Wy6VSuXPfe5zK7nqlWfq1Knl9ddfv1xTU1Pef//9y++99165XF78k0gfffRR+aqrrip36tSpXFNTU/7kJz9Znjt3bjVLbzE++OCD8jXXXFMePHhwef/99y/vvvvu5YkTJ9Y75s033yw///zz5QkTJlSpypZjxIgR5VKpVG7Xrl35tddeq9y/tE+/XXjhhZV9d91118out8XYdNNNyzU1NeXrr7++3v0NfWrw0ksvLZdKpfLWW2+9ssqsmsbOgDFDg5Xt4YcfLg8ePLjy93PBLI5OnTqVjz/++PLo0aPL5XK5PG3atPIVV1xR3mqrrSrP1bPPPru6xVfZqFGjyoMGDSqvvfba9V7DrbfeeuUTTjih3K9fv3qzYjbbbLPyG2+8Ue2yq+b1118vn3vuueUePXos9on6vffeu3zDDTeU586dW37ppZfK3/3ud8vrr79+Zf+vf/3rapdfFXq2ZG+//Xa5X79+5dra2vLpp59eHjt2bHnWrFnVLmuVNWvWrPIf/vCH8h577FFu1apVvb8FPXv2LJ933nnlyZMnl8vlcnn+/Pnle+65p7z77rtXnmunnHJKdR/ACnbOOeeUf/jDH5bvueeeapeySrnrrrsqz5FvfOMb5fnz5y/12Pnz55e/8Y1vVI43m4Gm8N4kC3MNmFVA9+7dM3ny5Dz00EPZZZddkny8xnOHDh0yf/783Hvvvdljjz3qnXPnnXfmgAMOWOMuvDxq1KjMnz8/3/ve9/LYY4+lb9+++drXvpbevXunffv2yzx/4Qtbr4n+7//+L7/73e/qreO84FdEnz59MmTIkPz3f/93OnXq1OA49957b/7rv/5rtb/uxP3335/99tsvdXV1adu2bfr375977rknpVIp++67b+XiflOnTk25XE7btm3z0EMPWXs9ySWXXJKzzjorH374YZJULtK36PU4/vSnP+XII49M27ZtM2nSpKy33nrVKrnq5s+fn8997nP55z//mc022yyXXnpp/uu//iutWrVKqVTKc889l969e2fMmDG56KKLct111yVJdtlllzz00EPVLb6KDj300Nx000056qij8pvf/KZy/9KuAVMulyt9HjJkSK688spqlL3SNHQtnCQNXnMOijZ58uTK9SRefvnlJP95HbLTTjtlyJAhOeyww5b4mm7evHk54ogj8uc//zk9e/asvI5ZU0yaNKnSu1deeSXJf/627rbbbhkyZEgOOuig1NbWJknGjx+fn/70p/ntb3+b5OPZ8yNGjKhW+SvdnDlzcvPNN+c3v/lNHnjggXoz0bp165ZBgwbl2GOPzSc/+cnFzp06dWoGDhyYhx56KNtss02ee+65lV1+VehZwxa+zteCn73GKpVKqaurWxFlrZIef/zx/OY3v6l3rZdyuZza2tocdNBBGTJkSHbfffelnn/SSSflsssuy2abbVb5fbg6WvAa7pZbbsmBBx5Y7XJWKXvuuWceeOCByjV0vvGNb2SXXXbJJz7xiSQfvy8yatSoDB06NE899VSSj98rWlOvZ0LTeW+yGNOnT899992XZ555JlOmTMmsWbMaXDmgVCpl2LBhK7HCJqhC6EMT7bPPPuWampryVVddVe/+z3zmM+Wamprycccdt9g5hx12WLlUKpU333zzlVVmi9DU9dYXvq3p69vvu+++5datW9f7ZNE666xTPvHEE8v/+Mc/mjTWSy+9tMZ8Yvrhhx8ub7bZZoutr77oNXI23XTT8uOPP17tcluEH/zgB5XetG3btrz99tsv9VP48+bNq6zxvOjvwDXRa6+9Vu7evXulXx07dqw81zbccMNyu3bt6j33evbsWX777berXXZV3XrrreVSqVRea6216q0/v7Tn3De/+c3KvkWvG7M6MgOGapszZ075+uuvL//Xf/3XYq9DNthgg/K3vvWt8gsvvNCosZ544ok15vpN5XK5PHv27PK1115b3nvvvRfrXdeuXcv/+7//u8wZpD/84Q/LpVKp3K1bt5VUdXU98cQT5f/3//5fed11163397JNmzblgw46qHzXXXc1+GnoBUaNGlV5HbO607PGWfS6mE25+RtbLk+ePLl8wQUXlLfeeuvF/o/69Kc/Xb744osbfd2qf/zjH+VSqVRu3br1Cq66urp06VKuqalZ7a+vtCK899575U996lONev9owXNwwYoXlMuvvvpq+cknnyyPGjWqPHLkyAZvayrvTS6fefPmlc8555x6s7kb87Pakv+etq52AMSy7bLLLrn33nvz4IMP5rjjjqvcf9hhh+Wf//xnhg8fnk984hM59NBDM2PGjIwYMSI33HBD5VP4a5qySV3Ncs8991S2d9lllwwZMiSHHHJI2rZt2+Sx2rdvn1133bVJn/xaVfXr1y8vvfRSrrvuuvzlL3/JmDFj8s4772TevHlZf/3106dPnxx44IE5+uij06ZNm2qXW3VPPfVUfvzjHydJjjzyyAwdOjSdO3eud12rhdXU1OSQQw7J0KFD87e//a3e78A10aabbpqnn346J598cm644YbKtUqS5N13361sl0qlHHroobn88svX+HWhBwwYkN133z0PPvhg9thjj/z4xz/OwQcfXNlfV1eXyZMn55FHHsnFF1+cRx99NKVSKQcddFC++MUvVrFyWDN84hOfyAcffJDkP58a32uvvTJkyJB85StfadL1m9Zff/0kWe0/ST569OiMGDEi1113XeX6LeVyOa1atcp+++2XIUOGZP/991/q39aFDRw4MOecc07lmpOrs2233TYvvvhikv/8v9CrV68MHjw4Rx99dDbccMNGj9W1a9ckH69KsDrTs8Y7++yzq13CKm3TTTfN/PnzK8+ztddeO4cffniGDBmS7bffvkljLVitYXW93tACPXv2zOjRo/PWW29Vu5RVznrrrZcnnngiZ511Vq666qrMnDlzicd16NAhJ5xwQs4999w1/tqQ//rXv3LeeeflL3/5S2Vm2rKs6bP7vDfZfMccc0z++Mc/Vl7frr/++nnnnXdSKpXSrVu3/Pvf/66splIqldKlS5dGzSyqJkuQrQIWLP/RsWPHTJo0qfKCYubMmdluu+0yceLExd7oLpfLWW+99fL000+nW7du1Si7KkaOHLlc5/fv37+gSlY9G2+8cY4++ugMGTIkW265ZbXLYTU1aNCg/Pa3v80Xv/jFPPzww5X7G1oG6brrrsvXvva1bLXVVhk3btzKLrnFeu2113LnnXcuMfQ74IAD0qtXr2qX2GJ88MEH2WOPPTJ27NhlBsPlcjk77rhj/va3v6VDhw4rqcLqsQTZivH+++/nN7/5Te67774899xzef/995N8/A//dtttlz333DODBg1ao5dVXGBBSLCsJYwa49///ncuvvjiJKv3m6ELfm4X/BvXo0ePHHvssTnmmGMqy6c01oQJE7LllluuET/jC55r7dq1y1e/+tUMGTKksrxzU7377rs5/fTTUyqV6i1vubrRM1aWBc+1ZS032RgffvhhbrrppiQfX6h+dXXRRRflW9/6Vo455pgMHz682uWssqZOnZoHHnggY8eOzZQpU5IkXbp0yec+97nsvvvu6dy5c5UrrL5bb701//3f/53Zs2c3KVRYE15bLI33Jpvvr3/9a/bdd9+USqUcffTR+eUvf5k333wzn/70p+s9p/71r3/l8ssvz6WXXpoePXrk1ltvTe/evatc/dIJYFYRI0eOTF1dXfr06VPvn/XXXnstRx55ZB555JF6x2+33Xb5/e9/n8985jMru1RWUXV1dWnd2qS4xqqpqUlNTU3OO++8nHHGGdUuZ5XRo0ePTJw4MX/84x9z+OGHV+5v6E3gxx9/PF/84hfTsWPHRn/aBhY1d+7c/PCHP8xll11W+cT4otq3b5+TTjopP/rRj9aYGWsLfvbuvffeJYbv//rXv7LPPvukVCpl4sSJjfqna9NNN10Rpa4yrrzyypx22mmVT1Mu2rMFIWD79u3zy1/+Mscff/xKr7ElOfjggzNkyJD813/91xoxc7YINTU1jb4WwrLMnj07TzzxRJLV/5/9vn37Nvp6hnxMz1hZvv3tb2fIkCHZeuutq13KKmPu3Ln5whe+kGeffTbXXHNNjjnmmGqXxGrojTfeyNZbb52ZM2dmk002yemnn5727dvn+OOPT6lUyn333Zf3338/Y8aMye9///tMnjw5O++8c84555y0atVqtX9tQfEOP/zw3HDDDdluu+3yz3/+M0nDHwq8/fbbc9BBB6V79+4ZO3Zsiw1NBTCriX/96195/vnnU1dXly233DJ9+vSpdkmwWmvbtm0++uijPPzww9lpp52qXc4qo3379pkzZ07GjBlT7/dUQwHM008/nc997nNZa621MmfOnJVdMquZGTNmZOTIkUucObTnnnu22BdsK8qCn72irOlLDfz0pz/NWWedVQldOnfunD59+mTjjTdOkrz11lsZO3ZsJQQslUo5//zz1+gg//XXX0+SdOzY0YygRho6dGiOPPLINX6JSWD1MWrUqCQfL0tpNYbGef311/Puu+9m8ODBefbZZ7PHHnvka1/7Wj796U9n3XXXTatWrRo8f03/wAyNc/rpp+eXv/xl1l577YwbNy5du3Zd6pvhs2bNyuDBg3P99dfn8MMPzx//+McqVl5dxx57bJJk3333zSGHHFLlalYtm222Wd54441cdtllOeGEE5Ise1WGIUOG5De/+U3OOeecfP/731/ZJTeKAAagGbbYYou89tprefzxx5u8LvGabJ111sn06dNz33331fvEbkMBzIIpqBtssEHefvvtlV0yrNYac42IpliTlxp47rnn0qdPn8ybNy+f+MQn8vOf/zyHHHLIYtcwqaury4033pjTTz89kydPTuvWrTN27Nhsu+22Vaq8uhb8/h86dGhOPPHEapcDUIiPPvoo//jHP5a4DOWCDxbxHwv+FgwbNsxMjkZa+EM0C66h1lhr+gdmaLw+ffrkn//8Z84444ycf/75SRp+M3z+/PnZYYcdMnbs2Nxwww31rr25JlkQgN51113ZZ599qlzNqmXBh3YXfs/oxRdfzDbbbJNSqZSZM2emtra23jn33HNP9ttvv3z2s5/NP/7xj2qUvUzWG2K19tRTTzW4/nrfvn2rXOHK96Mf/WiFjPuDH/xghYzbUu266675/e9/n6eeekoA0wSbbrppnn/++bz00kuNXjLlgQceSJJstdVWK7K0Vcr8+fPzwgsv5JVXXsn06dMb9Yb3UUcdtRIqY1WzOq+NvrJdcsklmTdvXjbYYIM89thjS/1kaevWrXPEEUdk5513zvbbb5933303l1xySS6//PKVXHHL0K5du8yePdvf0hVgzpw5+eCDD7LBBhsUHrYCSzZz5syce+65ufrqq/Pvf/97icesu+66Of744/O9732vxV80eGXp2LFjZsyYkU996lPVLmWVsvDnqX22ur4Fs6qSj/93X9L9zbHwWGuCiRMnJkm++MUvVu5bOOxbdCn7mpqafOMb36hcm2hNDWA22GCDvPvuu9loo42qXcoqa+GZ8WuvvXZl+5133kn37t3rHbvhhhsm+c/ztSUSwLBaevbZZ3P88cdn9OjRSz3mf//3f/OFL3whV1555Rr1Qu+cc85ZIWusr2kBzMknn5w//elP+cUvfpGvfe1r1sVupD322CPPPfdcrrjiikZd9+DNN9/MVVddlVKplL333nslVNiyzZw5Mz/+8Y9zzTXX5L333mv0eaVSaY0OYBYscbTRRhst9mmZRc2ePTvvvPNOkjVjaQYXQi7OAw88kFKplDPPPLNRz53u3bvnO9/5Tr797W/n/vvvXwkVtkybbLJJJkyYsMbOnGqODz/8sPIG0q677pqOHTvW2z9lypSccMIJueOOO1JXV5eOHTtmyJAhOe+885b5O3BNUFdXlzvvvDN///vfG/1BhlKptFr/nPqAVjFef/317LnnnpkwYUKDb4a///77ueCCC3LTTTfl/vvvT7du3VZilS3TpptumnHjxlWun8ayeQ3XsN122y2lUmmx2T4L7m+ONXHm0IwZM5Kk3hveCwfHU6dOzfrrr1/vnAWzup955pmVUGHLtM0222TkyJF57bXX8tnPfrba5axSNtpoo7z++uuVD9EvuK9Nmzb56KOP8s9//nOxAOa1115L8vH/8i2VAGYV88wzzzT5n4Vhw4atpOpahvvuuy8HHHBA5s6dW3nhu9Zaa1X+KLz33nv56KOPknx8ce8ddtghd9xxR/bYY4+q1byyLevTMaVSqZBjVmd9+/bN0KFDc9JJJ6V///659NJL630qhCU76aSTctlll+WZZ57Jueee2+D6nP/617/y1a9+NVOnTk2HDh0q63+uqT788MPsvvvu+cc//rFG/+w11b333pt99903HTt2zMSJE5f55uPMmTOz7bbbZtasWbnvvvuy2267rZxCWeW9+eabSdKkvwX9+vVLkkyePHmF1LQq2HvvvXP55Zfn4Ycfzo477ljtclYJN910UwYNGpRu3bot9km/+fPnZ9999633t2L69Om56KKLMnHixNx0001VqLjlePjhh/M///M/lWA+afh18YLXuyviw0stiQ9oLb+PPvoo++67b15++eUkSe/evTNo0KB84QtfqHcdsNGjR2fEiBF54YUX8tJLL2XffffN2LFj632CfE20//77Z9y4cbnvvvuyyy67VLucVYJZzMu2tN/v/pdqvM6dO+f999+v98b2woHLhAkTFgtgFlzrcMqUKSunyBboyCOPzEMPPZTf/va3GTBgQLXLWaV86lOfyuuvv54XXnihsmpK69at06dPn4wePTq/+c1vsv/++9c7Z8FKAp/85CdXer2NtWb/lV+FjBs3LoMHD84TTzzR6HMW/LOwJgUwU6ZMySGHHJI5c+akpqYmgwcPznHHHZc+ffpUXtTOmzcvY8eOzdVXX53hw4dnzpw5OeSQQ/LSSy8t9odjdTR//vyl7ps4cWIOO+ywPPnkk9l3331z7LHHZocddqhMm3z77bfz5JNPZtiwYbn77ruz/fbb5/rrr89mm222kqpvORZcVG2rrbbKM888k1122SXdu3dv1EUP17Sfy4X16NEjP/nJT3LGGWfknHPOyZ133pmDDjqosv/GG2/MWmutlUceeST33ntv5s+fn1KplIsuuigbbLBBFSuvvh//+Md56qmnkvz/7d13VFRX1wfg3xkEEUSliGJBLNgQEbALWBALVhRUFLBEjUajUWOSN8YSo4mJLUYjdhAEK1bsSrWhCIJgV7ABAoqRXu/3B99cQdpQ7wyzn7Vcr7lzz6ztvFPuPfucvYHevXtj9uzZMDIyQqNGjai0TCmOHj0KjuMwduxYiRpWa2hoYPz48XBzc8Phw4cpAUMkJv7eL8/KSPFCGnn+DC9cuBCurq7YsGED7O3t0bx5c6FDknoXL14EANjY2BR57xw+fBh3794FYwwmJibo378//P39ERISgpMnT+LChQsYNmyYEGEL7tGjRxg2bBjS09PBcRyUlJSgr68PDQ0Nuf4MitGEZOXs2bMHDx8+BGMMP//8M1atWlXkfqB9+/awsLDA4sWLsWrVKqxZswYPHjzAnj17MGfOHIEilw6LFi3Cvn378Pfff8POzg5dunQROiQi43x9fct1nBSvQ4cOuHnzJl68eMEvlFFTU0OrVq3w6tUrXLp0CT179iw05vLlywDy+7/Kq+nTp+PgwYM4deoUVq1ahZUrV9b6xRxVZcCAAfD29saVK1cwb948/riDgwOCgoJw4sQJTJ06FRMmTEBqair279+PK1eugDEm3ckujki958+fcxoaGpxIJOIYYxxjjGvQoAGnq6vL6enplflHnvzyyy8cY4yrW7cud+HChTLPv3jxIqekpMSJRCJu+fLlNRCh9Pr48SPXrl07rk6dOpy7u3uZ5x84cIBTUFDg2rVrx338+LEGIpQujDFOJBLxf8SfzYLHivsjPkfe/fXXX5ySklKprxljjKtTpw73999/Cx2uVGjbti0nEom4kSNHcrm5uUKHIzMMDAw4kUjEHThwQOIxnp6eHGOMMzIyqr7AZFBqairn7+/P+fv7Cx2KVBK/11avXi3xmN9++41jjHEGBgbVGJn0O3XqFKempsa1aNGC8/Dw4DIzM4UOSaoZGhpyIpGIO3ToUJHHhg0bxjHGuB49enDZ2dkcx3FcVlYW16tXL04kEnGTJk2q6XClhqOjI39tsXbtWi45OVnokEgtMnDgQE4kEnHjxo2TeMy4ceM4xhg3aNCgaoxMdty6dYtr3rw516BBA27t2rVcVFSU0CERIve+//57TiQScd9++22h4/Pnz+fnJn18fPjjhw8f5urVq1fu78Paxt/fn7tw4QJnZGTEiUQirmPHjtzKlSs5Ly8vzsfHh7+nKumPPHvx4gXHGOOUlZW5uLg4/nh2djZnampa7BwSY4zT09PjPnz4IGDkpWMcR0tdpJ2DgwM8PT0hEomwZMkSzJ07Vy53HEjCxMQEYWFhWLRoETZs2CDRmO+//x6bNm2CsbExv7pcHq1atQqrV6/GnDlzsH37donGzJ07Fzt37sQvv/xSbbWjpZWenl6lVjBERUVVYTSy6eHDh9iwYQO8vb2RkJBQ6LGGDRvC2toa//vf/2gF3P+rV68esrKycOHCBVhZWQkdjsyoX78+0tPTcevWLYmbfN+5cwe9evWCmpoav4WeAJGRkTA0NIRIJJK7+teSWLRoEbZs2QI1NTVcu3atzP5yERER6NevH1JSUrBw4UJs2rSphiKVLoMGDQKQX7s5KioKjDF+Z4IkO0prc1+OkjRt2hQJCQm4efNmoVWn2dnZaNSoETIyMrBv375C5WlcXV0xY8YMtG7dGs+fPxcibMG1aNECsbGx5bpPIERS2traeP/+PU6fPl2kNEpJzp07h5EjR0JLS4vvPyev2rRpAyC/5G5iYiJ/n1W/fn00atSozN8Cef1eK+jdu3fw8/NDREQE3ztBQ0MDXbp0wYABA6gZOKkQX19fWFpaolmzZnj58iX/WXz16hU6d+6M9PR0APnvtYyMDKSlpYHjOCgoKCAwMFBuy8uKRCLqNVQJ0dHRyM3NRbNmzVCvXj3+eFJSEhYsWIAjR47wrSUYY7C2toazs7NU91SjBIwMEN9k0c1C2dTV1fHp0yf4+vrCwsJCojEBAQEYMGAAGjZsiKSkpGqOUHoZGhriwYMHuHLlCl9nsSziH+POnTsjIiKimiMktdmrV68QHx+P3NxcaGpqok2bNlQO5AutWrXCmzdvEBwcDGNjY6HDkRnKysrIzs7GnTt3YGJiItGYkJAQdO/eHYqKisjMzKzmCGWHOAHDGKOG6cV4+fIlOnbsiKysLNSvXx/Lly/H9OnTi5Q3ff/+PVxcXLB27Vr8999/UFZWxqNHj6CrqytQ5MIqeIMq6W1Jwb4c8vheVFJSQm5uLu7evVuoseuNGzdgZmYGxhhiYmIKTbaJH6tXrx7fUFfeiH8PAgIC+P5LhFSVunXrIicnp1zXaaGhoTA1NYWSkpJUNw6uCZW57pfX3wKx2NhYLF68GMePHy9x0rZOnToYP348Nm7cCB0dnRqOkMgyjuOwevVq5OTkYNasWYWuV8+fP48pU6bg48ePhcbUrVsXzs7OmDZtWs0GK0XoO616JScn4+nTp8jJyUG7du2goaEhdEhloh4wMuDTp08AgPHjxwscifQTX7iqqqpKPEZ8rrxPsombuDZs2FDiMeJzX758WR0hETmiq6srt5OPkurZsyfevHmDx48fUwKmHLS0tBAbG4sXL15InIB58eIFAMjEhRyRHq1atcLOnTsxffp0pKSk4Mcff8RPP/2E1q1bQ1tbG4wxvHv3DlFRUeA4jk8g7Ny5U66//ywsLKgmdjmpqKggOTm5yIr5gIAAAEC7du2KrHQuuHpQXjVu3BgxMTH0WpBqoaGhgfj4eERFRUl8nSbeEU/XG9RQvqLCwsIwePBgfPjwodRFDNnZ2Th8+DCuXLmCq1evlrlLV96dOXMGR44cQWJiIlq3bo2ZM2dKfB8hy3bu3AkLCwt06tSJP8YYw8qVK4s9f/jw4Xj69CmOHTuGyMhI5OTkQF9fHxMmTJD7nn7Ua6h6qampydxnkhIwMqBly5Z49uwZ30SelKxp06Z49eoVv5pIEqGhoQAg91tyFRUVAQD379+X+Ivs/v37hcYSQqrPokWLcPz4cWzbtg0TJ06kCUsJdevWDbGxsTh8+DBsbW0lGnPo0CEAoPJ3pNycnJygqamJr7/+GjExMeA4Ds+fP+eTegUnR5o1a4Zdu3bB2tpaqHClgp+fn9AhyJy2bdvi3r178PPzw5AhQ/jjJ06cAGOs2F3g4lKf2traNRantDEzM8ORI0cQEREhczftRPqZmJjg/Pnz+PfffzFu3DiJxmzfvh2MMVpYA8DFxUXoEGROamoqRowYgffv3wMABg8ejFmzZqFXr15o2rQpACAuLg63b9/Gnj17cOnSJSQmJmLEiBF49OgRVFRUhAxfML6+vpg4cSKUlZURHh5epFH88uXL8fvvvxc6tmfPHuzduxeOjo41GGnNmzt3Lhhj0NLSgpmZGSwsLGBhYYFu3bqVeO8pvu4lhfXv31/oEIiUoRl9GTB06FA8e/YMt2/flrh+vbwyNzfHgQMHsG7dOkyYMAENGjQo9fzk5GT8+eefYIzB3Ny8hqKUTkZGRvD398eff/4JOzu7Mi/I0tLS+Neua9euNRQlIfKrb9+++PPPP/HDDz9g0qRJ2LlzZ5EbBlLUmDFjcO7cORw/fhxHjx6FnZ1dqecfOXIEx48fB2MMY8eOrZkgSa0yYsQIREdH48SJE7hy5UqxtdgHDx6MsWPH0gIGUiFWVlYIDQ3F9u3bYW5uDnNzc7i4uODOnTtgjGHUqFFFxoSHhwPIT/zJq8WLF8PLywtbtmzB5MmTaXFbAeJeTF/2VRIfrwh569Fkb2+P8+fPw8/PDzNmzMDWrVtLrMqQlpaGBQsWwMfHB4wxTJ48uYajJbXBtm3bEBMTA5FIhJ07d+Krr74qco64yoCtrS327duHWbNm4e3bt/j333+xdOlSAaIW3rlz55CYmAgbG5si91Lh4eH4/fff+QUz6urqSEpKQk5ODr7++muYm5vX+n7MHMchISEBJ0+exMmTJwEADRo0QN++ffmETI8ePeg3lFSb9PR0HD16FED+LqvGjRuXen5CQgLOnz8PIP+3WFrvr6gHjAx4+fIljIyM0KhRI4SEhNAW5VJcv34d5ubmYIzB0NAQe/bsQffu3Ys9Nzg4GLNnz8a9e/fAGJP7etCenp5wcHAAYwympqbYtWtXobriBYWFhWH27Nn8jb67u7vc3Ti8evWqUuNre7mZGTNmVPlzMsawd+/eKn9eWXPixAnMmjULmZmZsLKyQvv27SVawbZixYoaiE76ZGVloWPHjoiOjoaCggIWLlyIhQsXomXLloXOe/36NTZv3oytW7ciLy8PLVu2xOPHj1G3bl2BIpc+1AOmdOLfhfr169O1GqlWsbGx6NSpE5KTkwsd5zgOnTt3xv3794usVB04cCACAgIwd+5cbNu2rSbDlSrbt2/HggULYG1tjX379kFLS0vokKSCuFb9l9/v4h5N5ZkykNceTRzHwdzcHDdu3ABjDI0bN8aECRPQq1evQmUog4KCcOTIESQkJIDjOJiZmfHlAwkpj759+yIoKAjTp0/Hnj17JBozc+ZM7Nu3D71798aNGzeqOULp1KdPH9y+fRvbtm3D3LlzCz02d+5c7Ny5E+rq6rhy5QqMjY0RHByMYcOGISkpCUuXLsW6desEirz6ubm5ITAwEIGBgXjy5EmhxwpeVygrK6NXr158QqZPnz5U3pNUmf3792P69Olo3rw5oqKiykz25eTkoHXr1oiJiYGHhwcmTZpUQ5GWDyVgZISPjw/Gjh2LZs2aYevWrbCyshI6JKk1f/58fjs3ABgYGBR74RsZGQkg/2J53rx52Lp1q5BhS4UJEybg2LFj/GtnaGiIHj16FHrt7ty5w5ce4zgO48eP57PT8kRBQaHCYxljJTZIrC0KNlWuCvJ4I1+c+Ph4fP/99zh48CDy8vLKNVaeX7t79+7BwsICKSkp/PtSV1eXb0IaGxvLT55zHIf69evD39+fSoJ8gRIwpRN/723duhXffPON0OHItDdv3iAuLg5paWno0aMH3dQXIzAwEJMmTUJsbCx/rE2bNvD29kbHjh0Lnfv8+XN06NABHMfBy8ur1u/uW716damPX7hwAbdu3UK9evVgZWWFjh07yv1ChgEDBvC/jwXr1hc8XhHyVgM/KSkJI0aMwK1btwCgxNdOPAXTp08feHt7Q11dvcZilCXv3r0rdhepvJcOF9PS0kJSUhIuXryIwYMHSzTm6tWrsLKygoaGBhITE6s5Qumkp6eH169fw9fXt0jJzpYtWyImJga//PILfv31V/74qlWrsHr1apiYmCA4OLimQxZEQkICn4wJDAxEWFhYoev/gt9vioqKMDEx4RMyZmZmZVajkSf//fcfjh07hps3b/LXty4uLmjVqhV/TkxMDD5+/AhlZWW0adNGwGiFZ2tri+PHj2Px4sXYsGGDRGOWLl2KjRs3YsKECXw5cWlDCRgZ8vz5c/Tt2xeJiYlQV1dHu3btyrxZkLet30D+Be2PP/6ITZs28ROUX178it/2IpEIS5Yswbp166ifAvInaJcsWYJt27aV+NoBnyfD582bh02bNsnl9lPxSsGKkIfJSz09vWr5TImblcqj9+/fo1+/fnj69Gm5VqKKlTdhU9s8fPgQDg4OfN8v4PP3W8HX09TUFO7u7kUmMAkpi6qqKjIyMnDr1i0qGVsBycnJ+Ouvv+Dq6oqYmBj++P3799G5c2f+vw8dOoTjx4+jYcOG2L17txChSo2srCxcv34dcXFx0NHRgZmZWbHXZNeuXePvB5YuXVrr6/5LughEfD0rqdp+7UaqRl5eHpydnbF9+3Y8fPiw2HM6deqEefPmYc6cOZW6p6iNOI7Drl27sG3bNjx48KDYczp37oxvv/0Ws2bNkut7eGVlZWRnZ+POnTsS97UKCQlB9+7dUbduXaSnp1dzhNJJfL0WGhpaqJT68+fPoa+vD8YY7t69W6gaiDhx1bBhQyQlJQkQtfBSUlJw48YNPiFz+/ZtZGRk8I8X/CyKRCIYGhqif//+2Lx5sxDhSo1t27Zh2bJlSElJAfD52uPL61txRRplZWW8efNGrnfTt2nTBi9fvsSJEycwevRoicacOXMGY8aMQdu2bfH06dNqjrBiKAEjI27cuAFHR0dER0dLNPEmr1u/C4qIiICzszOuXLlS5AOor6+PwYMHY+7cudRkuRgRERHYsWMH/9oVfM+JX7uvv/5arnu/7N+/v8xzUlNT8eTJE3h5eeHt27fo168fZs6cCQCYOnVqdYdIapklS5bwF7C2trb45ptv+PKU8nzzWV6XL1+Gt7c3QkND+ZV/WlpaMDExwahRo2BpaSlwhERWtW/fHs+fP8f169fRu3dvocORKU+fPoW1tTVevHhR6JqjuBvU6OhotGvXDhzHwd/fH2ZmZkKETKRYdU1oy/tCBlJ+sbGxxe7gEO/AJYUlJSVh9OjRfGmskuY9xNe9ffv2xZkzZ+S2J6J4t4abmxumTJki0RjxJG/z5s3x+vXrao5QOqmoqCAzMxN+fn6F+gC7urpixowZaNSoEf+ZFbt37x5MTEygqKiIzMzMmg5ZKomTf4GBgQgICMCNGzfw33//FTpHnucjAWDlypVYs2YNOI5D3bp1YWhoiODg4GKvb/Py8tCiRQu8e/cOO3bswKxZswSMXFj16tVDVlYWgoODJa5IERoaClNTU9SrVw+pqanVHGHFyN+ydRn04MEDDB06FGlpaeA4DsrKytDX10ejRo1oxUwpunTpgn///RdA/gpB8UoFdXV1KCkpCRma1OvSpQtfH7zga9eoUSPqh/D/ypNAWb9+PRYtWgRnZ2f069evVteNJdXn9OnTYIzBwcFBogQgKZ6VlRWV8STVYsiQIXB2dsa1a9coAVMOGRkZGDFiBJ4/fw5VVVXMmzcPFhYWGDlyZLHn6+npYeDAgfDx8cHp06cpAUOKoEQJkRY6OjqUbJEQx3EYM2YMrl+/DgDQ1NTke+g0bdoUABAXF4fbt2/jyJEjSExMxI0bNzBmzBj4+/sLGbpgevfuDS8vL2zatAkTJ06UqE/Cpk2bwBiT6+uUpk2b4uXLl3j48GGhBMzFixcBoNi+wOIJXSoX+JmioiL69u2Lvn374scff0RWVhZcXV3x119/ISoqqkIVG2qTu3fvYs2aNQAABwcHbN26FQ0bNixxDlckEsHOzg5bt27F5cuX5ToBIy73X55kZ1ZWFoCSE/fSgBIwMuDXX39Famoq6tati40bN2LGjBlQVlYWOiyZoqSkRLViK4heu8pTVFTEtm3b8PDhQ6xfvx4DBw7E0KFDhQ6LyJi3b98CAGbMmCFwJISQ4ixcuBCurq7YsGED7O3t0bx5c6FDkgnOzs549uwZVFVVERgYWKjkR0mGDx+Oq1ev4ubNm9UfICGEkGrn6emJa9eugTGGyZMnY/v27VBTUytynpOTE9atW4d58+bB3d0d165dw8GDB2Fvby9A1MJycnKCl5cX7t27hxEjRsDFxQXNmjUr9tyYmBh89dVXCAkJAWMM06ZNq9lgpUjv3r0RHR0NZ2dnODg4QEVFBS9evMCpU6fAGCt2oZa4Ib04GUjyJ8dv3bqFgIAABAYG4tatW3yiSponwWvKtm3bwHEc+vbtCzc3N4nG9OnTB1u3buV7LsurJk2aIDo6GhERERIni8WvWePGjasztEqhBIwMuH79Ohhj+Pnnn6mpKyEy7Ouvv4avry+2bt1KCRhSblpaWnj79m2xN6OEVIR4dRFjDDk5OUWOV8SXzyVP9PX1+dIevXv3xp9//glbW1vadVuG48ePgzGGhQsXSpR8AQAjIyMAkNoazzWJ4zjcu3cPYWFhSExMRHp6epkTH7W5mTypXsnJyYiKikJycrJEZWW+bHBNSEk8PT0BAP3794e7u3up59avXx/79+/Hq1ev4O/vjwMHDshlAmbUqFEYO3YsTp48iStXrqBNmzYYMmQIevXqBW1tbTDG8O7dOwQFBeHy5cv8CnEbGxuMGDFC4OiFM3PmTBw6dAjh4eHo0qULTExMEBAQgIyMDKioqGDy5MlFxgQEBADILzcrr/777z9cv36dLzl29+5dZGdnA/iccFFQUIChoSHMzMxgZmZWaIeRvAkICABjDPPnz5d4jJ6eHoDPCy/lVd++fREVFYXdu3fzJfzLsnPnTqnf3UcJGBkgLv80bNgwgSMh8uLp06dwc3PDzZs3ERcXh/T0dFy8eBHt2rXjz4mIiMCrV6+gqqqK/v37Cxit7NDX1wcABAcHCxwJkUXm5uY4dOgQIiIiJG60SYqiyaPPSpqkpVVrFTNo0CAA+SuvoqKi4OjoiK+++gr6+vpQV1cvNbHFGOObpMsbcZPqIUOGSDxGU1MTAPDx48fqCElm7N+/H7/++itevnxZrnGUgCHlwXEcdu/eDWdnZ4SHh0s8rrYm5FevXs3/veBnqeDxipD3z6V4Z0Z5Jiu//fZb+Pv7IzQ0tBojk24HDx6Ek5MTjh49iqysLJw9exZnz54tcp742s7Ozk7i1fi11aBBg7Bw4UJs2bIF0dHRePnyJf/6rF+/HlpaWoXOz8jI4HfH1Ob7gi/FxcUhMDCQT7hERETwr5P4f1VUVNCzZ08+4dKnTx9aLPj/YmNjAQAdOnSQeIy40pG89xmaPHkyPDw8EBwcjIULF+Lvv/8usectx3H47rvvcPfuXX4HpbSiBIwMaNGiBZ49eybXzau+JJ7k+HLCQny8IuR58kMsLy8PP/zwA7Zs2YK8vDz+h5Uxxq+YEXv16hVGjhyJOnXqICoqikqtSEDclO7L5nS1UZs2bar8ORljeP78eZU/r6xYsmQJvLy8sGHDBkyYMIFKUZbT7t27sX37dty/f1/iBENtnTwSW7lyZbmOk9L5+fkVujngOA6ZmZmIiIgocQxjDBzHlXhTIQ9SUlIA5K9olpT4xlRRUbFaYpIFy5Ytw7p16yT6PhO/z8hn79+/x/79+3HlyhXcv3+/SJP0wYMHY+rUqUUm4uRNdnY2xo4diwsXLgCgBD0ArFq1iv/OLpg0KXi8IuQ9ASP+DLZu3VriMeJzv2yYLk/q1q2Lw4cPw8nJCdu3b4e/vz/S0tIKnaOiooL+/ftj3rx5sLa2FihS6bJ582ZYWlri6NGjiIuLg46ODpycnIqdTzp9+jQaNGiAhg0bYtSoUQJEW3NcXV35pEvB+27xd7+Wlhb69evH724xMTEps/eQvFJSUkJmZma5Fgu9e/cOQH7vZXk2fPhwDBo0CD4+Pti2bRtu3ryJBQsWwNzcnO+rFhsbi4CAAGzdupVPvlhYWGDMmDECR18y+qTIgFGjRmHz5s0ICAiQ6u1UNcnPzw8Ailzkiic/ynNzQJMfn3399dfYt28fOI5D8+bN0adPHxw7dqzYc62trdG6dWtER0fj2LFjWLhwYQ1HK3vEjdPloRlndHR0lT+nvH9GTUxMsGfPHsycORNDhgzBnj175HobvKRyc3Mxfvx4nDlzBgBNHhVECZiqZWFhIfffUxWhqamJuLg4REdHS7y7LzIyEoD81mIPCgrCH3/8wdeqX79+PfLy8mBiYsInjj98+IDg4GA4Ozvj9OnTMDMzw9GjR6mvH4CtW7di2bJlxdaqf/v2LWJiYnDp0iWsWrUKa9asketr3I0bN+L8+fMAgFatWmHq1KkwMjJCo0aNSmwkLA9oB2nVa9iwId6/f4+YmBgYGxtLNEa8wrxBgwbVGZpMGDFiBEaMGIHc3Fy8ePGiUFK5TZs2lSovW1uNHDkSI0eOLPO8CRMmYMKECTUQkfBmzJhRaD6tbdu2/O4WMzOzcu3mkHe6urqIjIzE06dPMXDgQInG+Pj4ACjfrpna6siRIxgwYAAiIiJw9+5dTJ06tcRzOY6DoaEhvLy8ajDC8qMEjAxYunQpPDw8sH79ekyYMIGvCyjPSprkoMmPirt69Sr27t3L9xv69ddfoaCgUOrNlZ2dHf766y/4+PjI9c1pWZ4+fYqNGzdi//79YIzJxcqj0n4gScXMmDEDANC5c2dcu3YNnTt3RteuXdG+fXuoqKiUOpYxhr1799ZEmFJnx44dOH36NID8hn7Tp0+HqakpNDQ05HryiFQ98eIQUj4mJiY4d+4cAgICMG7cOInGuLm5gTGGPn36VHN00snZ2RlA/oT42bNnUadOHT4pBeR/52tqamLo0KEYOnQonJ2dMW/ePAwbNgxBQUFy3Zdo6dKl2LRpEz+51KhRIxgbG/OJqXfv3uHevXtISkpCamoqFi9ejJcvX2LTpk1Chi0YcS+OPn364MqVK6hXr57AEQkvLy+vXMeJZLp06QJ/f3+4uLhI3J/ExcWFH0vyKSgo8GWvCamoOnXqwM7ODra2tjAzM5PqxubSytLSEhEREdixYwdmz55d5vlv377Frl27wBgrV1ne2kpDQwNBQUFYtmwZdu3aVWRnn5iqqiq+/vpr/Pbbb1J/jcI4WqYhE8LDw2FjY4OUlBSsWbMGdnZ2cr8tjVStiRMn4ujRoxgxYgS/UhwARCIRGGO4f/8+OnfuXGjM8ePHYWtrizZt2uDZs2c1HbKgJCmxlZeXh48fPyI5ORlAfma+SZMmCAkJkYtdMKRqiT+LYpLu3BOfJ69lLHv16oU7d+6gc+fOCAwMhLq6utAhkVrq1atXAPJLaWloaAgcjezYv38/pk+fDmVlZTx69Ai6uroASr7++Pvvv7F48WIwxuDt7Y3hw4cLFbpg2rdvj+fPn2Pjxo347rvvAOTvCjI0NCzx+97Ozg7Hjx8vNEbeXLx4kX+/tGjRAhs3boSNjU2R8im5ubk4fvw4li5dilevXoExhvPnz8vlhIiKigoyMzNx6tQpiVaKE1JR27dvx/z588EYw/Lly7Fy5cpSr3N/++03/pxt27Zh7ty5NRgtIbWTpqYm34O64OdPX1+fLztmZmaGtm3bChWizHj+/Dk6d+6MnJwcrFq1CsuXLwdQ/PXt48ePYWtri8jISKiqquLFixeU9Crgv//+g4+PD0JDQ5GYmAggvxyeiYkJBg4ciIYNGwocoWQoASMDxBO9aWlpiI+PB2MMjDFoaWlJtOpZnnsmEMnp6uri7du38PLywtixY/njpSVgbt++jd69e0NFRYWv4S4vKrJyvk+fPti3bx9tKSUVoqenV6kdflFRUVUYjexo0KABUlNT4enpiYkTJwodjswQJxOaNGmCunXrlnpuRkYG4uPjAYCfPJdH4t/LrVu34ptvvhE6HJkhLp0VHh4OPT09/Pvvvxg2bBgUFBTAGENERAQ6duyI4OBg/P333zh06BAAwNzcXG53HampqSEtLQ0XLlyAlZUVAODhw4cwMDAAYwwZGRlF+uOcPn0aY8eORa9evXDz5k0hwhbcyJEjce7cOTRr1gx37twpczFMXFwcunfvjtjYWAwbNqzYxta1XfPmzREXF4e7d++iW7duQodDarHs7Gx07doVjx8/BmMMBgYGmDZtGnr16gVtbW0wxvDu3TsEBQVh//79fEPwTp06ISwsjPpQAAgJCSm1r5WpqanAEUoH8TVuRdX2a93IyEgEBgYiICAAgYGBePv2Lf+Y+F60SZMmhUqTGRsbUyWaYmzYsAE//PADGGPo0aMHxo0bh59++gmMMaxYsQKKioq4fv06Ll26xO+i3LVrF7766iuBIyfVgRIwMqAyJVLkedUzKR9lZWVkZ2cXucEqLQETGhoKU1NTKCkpISMjo4YjFtb06dPLPEckEkFNTQ2tW7dG//796caVEAGIEzA0eSS5S5cuYfjw4ahfvz6io6PL3DX04cMHtGrVCunp6bhy5QoGDBhQM4FKGVVVVWRkZODWrVvo0aOH0OHIlFevXsHMzAxv3rwBYwwqKip8qQEtLS0kJycjMzMTQP6uvrZt2+L69evQ1tYWMmzBiK/ZQkJCYGRkBAB4/fo1WrVqBcYYXr9+jWbNmhUaExISgu7du0NTUxMJCQlChC04bW1tvH//Hv/88w/mzZsn0Zh///0X3377LbS0tPhEszyxtrbGxYsXiyzQIkUNGjQIjDHs27cPrVq1kmhMTEwMHBwcwBjD1atXqzlC6RcdHQ1LS0tERUWVOZnLcRzatGkDHx+fWj8hXpaQkBB88803uHPnTqnnde/eHf/++y+6d+9eQ5FJp8r0wxH3WZMn0dHRfDImMDAQT5484R8Tf07r16+P3r1787tkevfuDWVlZaFClirr16/HL7/8guzs7BK/1ziOg4KCAjZs2ECl/WsxWiYgA6iXguREIhFEIhHCw8OLJAtK8vz5c+jr60MkEsndj2lBqqqq+PjxY7luyt+8eQMAcllqRVxzmBAi3fT19XHv3j1+JSAp29GjR8FxHMaOHStRyTYNDQ2MHz8ebm5uOHz4sNwmYJo3b47nz5/TwpcK0NXVxb179/Dtt9/iyJEjfHN0AIWuSxhjmDBhApydneW6nGDjxo0RExODT58+8ceaNGkCBQUF5OXl4eHDh0USMOJm1eKyqPJI/L7q1auXxGPE5xZ8T8qT+fPn48KFC9i7dy8lYMrg5+cHxli53ivp6en8OJK/2zs8PByrVq3C3r178fHjx2LPa9SoEWbOnIkVK1agfv36NRuklDl27BgcHByQnZ3N97ZSUlLi788/fPiArKwsAMCdO3fQr18/HDhwAHZ2doLFLDRag14+enp60NPTg5OTE4D867KCCZmwsDAkJyfj8uXLuHLlCoD8HjLGxsYwNzfH+vXrhQxfcEuXLsXIkSOxYcMGeHt7F5lva9iwIaytrfG///2P+lnVcpSAkQE00Vs+Ff1Blfcf4jZt2iAkJAQPHjzgy1mU5fz58wAAAwOD6gyNyKCCW7sLrkqjLd+kpk2aNAmhoaHw9vbGoEGDhA5HJty8ebPcDSCHDh0KNzc3uS1tBABDhgyBs7Mzrl27ht69ewsdjszR0NCAh4cHfv/9d5w9exbBwcGIj49Hbm4uNDU1YWxsjFGjRqF9+/ZChyo4AwMDxMTE4NGjRzA3NweQP+FmYGCA+/fv4/Dhw7C0tCw0RtxM/cvEjDxp0aIFnj17xu+mkoT43ObNm1dXWFLN2toa3377LbZu3Yrvv/8ef/75Z6VWjxNSFlVVVaxfvx5r167F3bt3ERERUaSclrgCg7x7/PgxHB0dkZWVhTp16mDWrFmYMWMGunXrxn9Oc3NzERYWhr1792L37t3Izs6Gk5MTDA0N0bFjR4H/BcKQZH4tNTUVT548gZeXF96+fYt+/fph5syZNRCd9GvcuDHGjx+P8ePHA8hf2HH9+nW+bFlwcDAyMzNx+/Zt3LlzR+4TMADQqVMn7N27F0D+fEjB69s2bdpUquqRLAsICOD/bmFhUezxiij4XNKEEjCE/D95X3k0ZMgQ3L17ly+1UNaPwIMHD+Dq6grGGKytrWsoSunh5uYGABg7diwaNGgg0ZiUlBQcP34cAPgVJLVV69atARTdpi0+XhHyuOWbVN6CBQvg6ekJZ2dn2NjY8JOVpGTR0dEAUK6J7nbt2gGQ315DALBw4UK4urpiw4YNsLe3l9sJ28pq1aoV9dApg7m5OS5dugRfX1/MmjWLPz5x4kSEh4dj37590NHRwYQJE5CamgpXV1ccOXIEjDG+Cb08GjFiBLZs2YLz58+jX79+Eo05d+4cP1ZebdmyBa1atcIvv/yCY8eOYdy4cWjfvn2ZvUiB2n+9W1ni3TJUqqcoJSUl9OnTB3369BE6FKn1559/IjMzE8rKyjh37lyxO5AVFBRgYmICExMTTJgwAcOHD0dmZib++usv7Nu3r+aDlgLlqTCzfv16LFq0CM7OzujXrx/WrVtXjZHJJjU1NbRp0wZv377F69evER0djZiYGLlf4FwSXV1dWlT6/wYMGMD3OC84zyM+XhHSPGdEPWBIrVJav5KSBAUFoU+fPlBTU8N///1XzRFKr3fv3qFdu3ZIS0vDV199he3bt6NOnTrFvqaXL1/G9OnTERMTA01NTURFRcnd9u+KvNfkqdydOIH3ZR8q6mlFhBAfH49x48YhODgYCxYswOTJk9GxY0ea8CiBuL/EnTt3YGJiItEYcX8JRUXFcq0ur21Onz4NBwcHNGzYEH/++SdsbW1plW4ZxDsjmzdvLvHK+ry8PL4MqjzexEZGRsLQ0BD169fHmzdv+IUgaWlp6NKlC6Kjo4vcuHIcBw0NDdy7dw8tWrQQImzBxcTEwNjYmC+VUlYS5saNGxg8eDDU1NQQEhIit0nV9PR0/P777/jnn3+QkpIi8ThpngSpDhW5N/jzzz/xv//9D/r6+nj8+HE1RyjdaHFb+enq6uLt27f48ccf8fvvv0s05ueff8a6devQokWLSlcmkCeWlpbw8/PDuXPnMHToUKHDERTHcbh37x5fgiwwMLBIWS3xNLM837/PmDEDjDGsWbMGOjo6Eo1JSEjAjz/+CMYYv2OmNpO3OSPaAUNqJUmzpampqdi6dSsAoG3bttUZktRr0qQJduzYAScnJ+zduxcXL14stNpvy5Yt4DgO169fx6NHj8BxHEQiEVxdXeUu+VJZ8pD3LmlrN5VULJt4AvLLiQtqGFkxBV83juOwceNGbNy4UaKx8vq6aWlpITY2Fi9evJA4AfPixQsA8tkTTExc4q5x48aIioqCo6MjvvrqK+jr60NdXb3Uz7A8N2DW09Mrd/++qKgouVnQUBwDAwP4+voiJyen0L9fRUUFvr6+cHBwwPXr1wuN6dKlC9zd3eU2+QLkl187d+4c7OzsYGlpiTlz5mDatGkwMjLi7x04jkNYWBj2798PZ2dnNGvWDMeOHZPb5EtaWhqGDBnCl5eUh2tYSc2YMaPY47/88gsaNWpU6tjMzEw8f/4cd+7cAWMM/fv3r4YIZcu0adPAGEP37t0l/i149+4dpk2bBpFIJJcJGPGkd3mqUYwYMQLr1q0rV99XAnz99dfw9fXF1q1b5S4Bk52djaCgID7ZcuPGjUL95L78XWjbti3Mzc1hYWEhtaWgaoK4WsySJUskTsB8+vSJHycPCRhfX99yHZd1lICRQUlJSQgLC0NiYiLS09PLvBCuzRcjbdq0Kfb4kCFDoKioWOrYzMxMxMfHIy8vD4wxjBo1qjpClClTpkyBoqIivv76a7x+/Ro7d+7kb0j37NkD4PMPbP369bF//365LslQXuJMfJ06tf+rt6St3eXZ8i2vSvpOp0mPivnydaPXsWzdunVDbGwsDh8+DFtbW4nGHDp0CADkunnkl42UOY5DZmYmIiIiShzDGAPHcXJfBpX695VfSRO2rVq1QmBgIB4/fozIyEjk5ORAX18fxsbGNRyh9BHfN6SlpSErKwtbt27F1q1b+YbVjDG8f/+eb1jNcRzS0tJK/R5kjOH58+c1Er8QNm3ahBs3bgAAevfujdmzZ8PIyAiNGjWS25r1YuJJsoI4jsOpU6ckGi/+/tLQ0MD//ve/Ko9Pnsjrb0Hjxo3x9u3bcu3orlu3LoD8xTZEcvr6+gCA4OBggSOpfikpKbhx4wYCAgIQGBiIO3fuFNrdXvDzxhhDly5dYGFhwSddJE02EFLStWxtXZRQ+2cBaxE/Pz+sXLkS165dk3gMY6xWJ2DEdeoL4jgOb9++Ldfz9O7dGz/88EMVRSXbJkyYAEtLS2zfvh1nzpzBvXv3Cq2uNDAwwOjRo7Fw4UJoa2sLGKnsEZcWkOcV4qRsK1euLNdxUjp63cpvzJgxOHfuHI4fP46jR4/Czs6u1POPHDmC48ePgzGGsWPH1kyQUsjCwkLuEyk1RXzzL+8TwKXp0KEDOnToIHQYUuXL+wbx+ygzMxOxsbHFjomPjy/1OWv7Z97T05PvHXT69Gn6zBWgq6tb6P//ly9fgjEGHR2dUhcCMsagrKwMHR0d9O3bF3PnzkWzZs1qIuRaR54WtxWnX79+OHLkCO7cuYPu3btLNOb27dsAADMzs+oMrdYRl6qv7SXru3fvjrCwMOTl5fHHCiZc6tSpA2NjYz7hYm5uDnV1dSFCrZUyMjIAfE6UktqFesDICGdnZ3z77bfgOK5cKzykuf5dVZg+fXqh/96/fz8YYxg9enSpW7+/vPAdNGhQrb+Bqqi8vDx8+PABubm50NDQKHNnUW0VEBBQ6L/FjcH27dtXZmN5cZmBDRs2ICoqCkOGDMH58+erM1ypJV59unjxYsyfP1/gaAghxcnKykLHjh0RHR0NBQUFLFy4EAsXLkTLli0Lnff69Wts3rwZW7duRV5eHlq2bInHjx/TTQMpl4r0TQgMDET//v3RqFEjfPjwoZojlD7UK6FivrxvqCq1ubyqiooKMjMzceHCBVhZWQkdjlSryHcZ+awir9+ZM2cwZswYNGnSpMQkam12584d9OvXD02aNEFISAgaN25c6vnx8fEwNTVFfHw8AgMD0bNnzxqKVPZNnz4d+/fvh56eHl92tzb6MsmurKyMnj178uXE+vTpA1VVVYGiky0V+U7btWsX5syZg1atWiEqKqqaIyQ1TT6XCsiYhw8fYsGCBeA4DoaGhli9ejUUFRUxYsQIMMbw7NkzfPjwAcHBwdi9ezdCQkJgZmaGnTt3QkVFRejwq9WXNzz79+8HAKxdu5YufKuISCSiLcr4nHApiOO4Eus/F0dcZubrr7+u6vBkxps3b5Cbm4tu3boJHQohpARKSko4fvw4LCwskJKSgs2bN2Pz5s3Q1dXlywrExsbyzVs5jkP9+vVx4sQJSr6QCpNkIUx2djaeP3+OtWvXAoDc7vCgXgkVU5sTJdVFXV0dcXFxdC8gAfEuSJqclMyXi9vE7ty5g8TExFLHFlzcxhiT2/uKHj16YOfOnZgzZw569eqFTZs2YfTo0UUm0fPy8nDmzBksXrwYCQkJcHZ2puSLhJ4+fYqNGzfyC33L029HFtWvXx/9+vXjEy49evSAkpKS0GHJhNWrVxd7fPv27WVWjhF/p50+fRqMMfTr1686QqxVzpw5gyNHjiAxMRGtW7fGzJkzJe5dKhTaASMDvvnmG+zYsQONGzfGs2fPoKamhsjISBgaGhbZ4cJxHH766SesX78egwYNwpUrVwSMvOb9+uuvAIB58+bRjQKpUlVRcqFFixb4+eefMWfOnCqISDbp6uri7du3uH37NkxNTYUOR6aIG3w7OjpW2ypeQgp6+PAhHBwcEBoayh8r2KhazNTUFO7u7ujYsWONx0hkj4KCQqH/Fr+XKrIT+a+//sKSJUuqJC5ZUpFVlc+fP4e+vn6t3x1PqtbYsWNx5swZeHp6YuLEiUKHQ2oR8feYWEV+C8SL27y8vOSyBKp4IWBoaCjCwsLAGIO6ujqMjY2hra0NxhjevXuHe/fu8btFjYyMSk1YyUPz75L6CBeUl5eHjx8/8s3mOY7jdxrV5h4neXl5VGqygqrqO01ZWRk3b96EkZFRlccoK3x9fTFx4kQoKysjPDy8SHWj5cuX4/fffy90TEFBAXv37oWjo2MNRlo+lICRAQYGBnj06BFWr16NZcuWAUCJCRixwYMHw9fXF7t37y7XCn1CSPH8/f35v3Mcx5et27t3b6klyAqWu/uyfI88srOzw/Hjx+Hi4iK3K3ArSlFREXl5ebhy5QoGDhwodDhEjly+fBne3t4IDQ3lV6VqaWnBxMQEo0aNgqWlpcARSi+O4/DixQt+4kNDQwNt2rSR67KnVXVjP2HCBHh4eBRJ6MiDiiRgnjx5go4dO0JRUbFQM11CSuPr64vBgwejb9++CAgIkOvvLlK1aHFb5Uk64SvpRLA4oVXbk/QVee/16dMH+/btk9udt6RsX76vilu0VpKC7RG+//57uU6+AMDSpUuxceNG2NjYwMvLq9Bj4eHhMDY25l9XdXV1JCUlAch/HR88eAA9Pb2aDlkiVIJMBrx58wYACm2nKvjjmZ2dXaQvx+zZs+Hj44MDBw5QAoaUy8OHD7Fr1y4EBgbixYsXSE5OLtSErTiMMeTk5NRQhMLo379/scd79uxJ5e7KYe7cufDy8sLmzZthb28vtz2FKkJbWxtxcXGl9rcipeM4Dvfu3UNYWBgSExORnp5e5kXxihUraig66WVlZUW1/8vp4sWL2LZtG/z8/JCWllboMRUVFQwcOBDz58/HkCFDBIpQOCtXriz037/++isYY5gzZ06pJRq+7N/Xtm3b6g61Vnn8+DGA/CQgIZIaOHAg1q5di59//hmTJk3Czp076TqEVAlfX1/+77S4rWJ0dXUpKVoBU6dOLfMckUgENTU1tG7dGv3795fbMndEcl/OmYkTpBERETRfVE7Xrl0DYwyDBw8u8pizszM4joO6ujquXLkCY2NjBAcHY9iwYUhKSsKOHTuwbt06AaIuG+2AkQF169ZFTk4OQkJC+Ezoy5cv0bp1azDGEBsbW+SGNSQkBN27d+cn7OTFjRs3YG5uDiUlJTx79gzNmzcv9fy3b9+ibdu2yMnJQVBQkNyXRFq3bh1WrFiB3NxciTL1YvKwUuZLL1++BAA0b94cdepQLrs8li1bhj/++ANWVlbYs2eP3N88Scra2hoXL16kMiAVtH//fvz666/8Z1dS8vbdJqn379+DMUaTuV/IysrCtGnTcPjwYQAlr3oTT5hMnDgRrq6ucl1fmxpXl+3LXgnivnT79u0rdaISKNwrISoqCkOGDMH58+erM1yZkZSUVK6EvDzu3BXXtD937hxu374NVVVVWFlZoX379hL1GpXHRQxZWVnw8PDAyZMnC72/SiMPi9nKQr8FhJDaRE9PD4wxXL58Ge3atRM6HJmip6eH169fw9fXFxYWFoUea9myJWJiYvDLL7/wLSgAYNWqVVi9ejVMTEwQHBxc0yFLhGYNZYCGhgbi4+ORmprKH2vcuDF/8/7kyZMiCRhxiZCPHz/WWJzS4NChQ+A4DiNHjiwz+QLkT56PGjUKXl5e8PT0lOsEzNGjR/Hzzz8DyL8ANjc3h5GRERo1akR1QIvRqlUroUOQSatXr0bdunVhaGiIy5cvo02bNujXrx+6du0KdXX1MkvJyOONvNjMmTNx4cIF7NixgxIw5bRs2TKsW7dOosQyY6xcCWh58u7dOyxfvhzHjx/nt3o3aNAAY8aMwerVq6GrqytwhMKbPHkyTpw4AY7jUKdOHVhZWaFXr15o2rQpACAuLg63b9/G5cuXkZ2djcOHDyMnJwdHjhwROHLhiBujt2jRQuBIpJc44VIQx3Hl2uUuLi3z9ddfV3V4MsfPzw8rV67EtWvXJB7DGJPLBMyqVav49x5jDKmpqTh16pTE4+Xtuu3JkycYO3YsHj9+TNcS5RQVFQUAEt3DE0KItIuOjhY6BJmVkJAAAEV23D5//hxv374FYww2NjaFHjM3N+fPkVockXoDBgzgRCIR5+rqWuh4hw4dOJFIxC1durTImDlz5nCMMa5FixY1FaZUMDY25kQiEbd3716Jx7i4uHCMMa579+7VGJn069evH/+eCQ8PFzocUksxxjiRSMT/+fK/y/oj7xwdHTnGGDd9+nQuJSVF6HBkwq1bt/j32ZAhQ7iwsDAuNDSUP5aXl8clJiZyFy5c4MaMGcMxxjhzc3MuLi5O6NBrxOvXrzkdHR1OR0eH2759e4nnPX/+nGvevDn/uS34RyQScRoaGlxoaGjNBS6FvL29+ddj0KBBXHR0dInnvnz5krO0tOTPP3v2bA1GSmTNl5+5ivxp2bIl5+zsLPQ/RXDbt2/nFBQUiv0uK+2PvF6DVPZ9J09SUlK4Nm3acIwxTkFBgbOxseFmz57Nv39WrFjBzZ8/n+vduzd/rF+/ftyqVau4VatWCR0+qaUSExO59+/fCx0GIYRIrF69epxIJOICAgIKHRfP3aqrqxcZI76/V1JSqqkwy412wMgAMzMz+Pv7IzAwsFC9ynHjxmHdunX4559/0KlTJ0yYMAGpqalwdXXFnj17wBjDoEGDBIy85omzneXZttyxY0cAwLNnz6olJlkRHh4Oxhh+++03GBoaCh2OzMjJycHZs2cL9cwpq2QRYwxXr16toQilD/fFisAv/5sUz83NDZaWlggPD8f+/ftx6tQpjBo1SuLdQ/K4chfIrxML5O9aO3v2LOrUqYPIyEj+ccYYNDU1MXToUAwdOhTOzs6YN28ehg0bhqCgoFpfGurChQuIi4uDkpISJkyYUOJ5kyZNQkxMDP/fLVu2RLNmzfDgwQMkJycjKSkJ9vb2uH//vtyWZXR1dQUAGBkZ4cKFC6X2uNLV1cX58+fRq1cvhIWFwcXFBdbW1jUUqex59+4dvL29kZiYiNatW2PkyJESlT+qLahXQtV4+PAhFixYAI7jYGhoiNWrV0NRUREjRowAYwzPnj3Dhw8fEBwcjN27dyMkJARmZmbYuXOnXL3fCiqrDyT5bMeOHYiKioKCggIuXryIQYMGITIyErt37waAQqVSQkND4ejoiFu3bmHSpEmYP3++UGHLlDNnzuDIkSP8b8HMmTML9ckl+WjH8mdflvCsCl+WRCKkONnZ2Xj69CkAoG3btqhbt26hxzMyMrBs2bJC32lz587Ft99+K0S4UqVp06Z4+fIlHj58yO9sAfJ7bAJAv379iowRV4xSV1evmSArQtj8D5GEePWupqYml56ezh9PTEzkNDU1i10lzhjjVFRUuAcPHggYec1TUlLiRCJRuVbhhoSEcIwxTlFRsfoCkwEaGhrlfu3kXWBgIKenp1fks1fa6kl5XkVJKqcyu4cUFBSEDl8w+vr6nEgk4jZv3swfi4iIKPWzaGtrW2RMbTVjxgyOMcYNGzasxHPOnDnDv16amprcxYsX+cfS0tL45xCJRNyhQ4dqImyp1KJFC04kEnEHDhyQeIyHh4dc7lgu6MGDB5ydnR03YcIELikpqcjjp06d4lRVVQt9p+nq6sr19Yr48xYZGSl0KDJl7ty5HGOM09bW5j59+sRxXMm/B3l5edwPP/zAMcY4S0tLIcIlMqZ///6cSCTiJk+ezB8r7XojPj6e09HR4RQVFbng4OCaDFUq+fj4cI0bN+ZatmxZ7G/BL7/8UuT6VlFRkXNzc6v5YAVAO5YrprzVFuieilSVw4cPcyKRiNPS0uIyMjKKPD5s2LAin1ORSMTNmzdPgGili729PccY47p168alpqZyHJf/3SbeGbNly5YiY/bt28cxxjgjI6MajlZy1NhBBvTq1QsuLi74888/+RUMAKCpqYmLFy9CT08PHMcV+qOtrY0TJ06gU6dOAkZe88TNgF+9eiXxmDdv3gAoWl9Q3rRv3x5AfmNlUrZHjx5h2LBhePXqFTiOg6KiIjp37gxzc3P079+/2D8WFhb8/xJSEQW/57/877L+yKvY2FgAgIGBAX+sYF+r7OzsImMcHR3BcRzfSL02u3//PhhjsLKyKvEcDw8P/u8bN27EkCFD+P+uV68e9uzZw++cLE9vgNpGXK+4Irtwxb375NHJkydx7NgxxMTEFLkWi4+Ph4ODA9LS0gp9n71+/RqjRo1CSkqKMEELLCoqCi9evOCv3Yhk/P39wRjDggULoKamVuq5jDH8+eefGDRoEHx9fbFv374aipLIqgcPHgBAkbr0Yl/uJmrcuDEWL16MnJwcbNu2rdrjk3bnzp1DYmIievToUeS3IDw8HL///jv/G9CoUSNwHIecnBx8/fXXctFrQbxj+cOHDxLtWBZf+7ds2RK9evWCmpoaOI7jdyzn5OTUVOhSoTz3THRPRarCxYsXwXEcxo4dW2T3y9mzZ/ndHC1atICNjQ2aN28OjuPg7OyMGzduCBGy1Jg5cyaA/O/+Ll26wNbWFr1790ZGRgbq1auHyZMnFxkj3u0mzdfG8lkjQgYVLD1WkKmpKR49egQfHx9ERkYiJycH+vr6GDp0qFxule/cuTPi4+Nx+vRpjB49WqIxJ0+eBAB06NChGiOTflOnTkVQUBBOnjwJS0tLocORer///jvS0tKgoKCAX3/9FQsWLED9+vWFDovUYuLmpKR8xAkWbW1t/ljBz2pCQgKaNWtWaIy4Gbg8lKYUT1oYGRmVeI6fnx8AoGHDhsVe8DLGMGPGDCxatAhhYWHVEaZMUFVVxcePH8u1kEG8sEYer9nErl69CsYYRo4cWeSx7du3IyUlBXXq1MFff/0FS0tLXLx4ET/99BNiYmKwe/duLFq0SICohdWqVSuhQ5BJ4kVXBUsWiRvMA/m/F1+WDpw9ezZ8fHxw4MABzJgxo2YCJTLp48ePAAp/PgtOuqWmphZJ/InLqPj7+1d/gFLu2rVrYIxh8ODBRR5zdnYGx3FQV1fHlStXYGxsjODgYAwbNgxJSUnYsWMH1q1bJ0DUNefmzZsAgIEDB0JTU7PYc7y9vREcHAzGGNTV1eHp6ckvmklPT8f8+fPh4uKCJ0+ewMvLCxMnTqyx+IVWr149jBkzBlZWVoUWYhFSXUJCQsAYQ//+/Ys8Jl7U0b59e9y+fRtqamr477//0LdvXzx69Ah79uxB3759azpkqTFo0CAsXLgQW7ZsQXR0NF6+fMknP9evXw8tLa1C52dkZODUqVNgjEn1YmdKwNQCioqKfO16eWdtbQ1fX1+4ublh6tSpheoFFicgIADu7u4l3vjLk6+++goHDx7Erl27MGTIEIwaNUrokKSaj48PGGNYuHAhfv75Z6HDIXKAJtwqpnHjxoiJicGnT5/4Y02aNIGCggLy8vLw8OHDIgkY8a6Z5OTkGo1VCOLX5csLWbHo6Gi8e/eOv6Atqa+JsbExABTqEyNvOnTogKCgIBw+fLjYCaTiiHdZyfMiEPGuZfF7qCAvLy8wxuDk5ITvvvsOAGBoaIinT59i9+7dOH36tFwmYEjFZGRkAECh73xVVVX+70lJSYWS9QDQrl07AJ93NxBSEhUVFSQnJxdK6hXcyfHq1atCu3ELiouLq+7wpF5xO5bFvL29wRjD/Pnz+d+K7t27Y/78+Vi9ejWuXLlSo7EKoap2LAcHByMiIgKnTp2SiwSMmpoakpOTkZ6ejsOHD8Pf3x+TJ0+Go6MjunbtKnR4pBaLj48H8Pk6QiwvL49ffPTtt9/yifmGDRti/vz5mDdvHp9wlWebN2+GpaUljh49iri4OOjo6MDJyanYPuenT59GgwYN0LBhQ6mex6QEjAwQv8EcHR0xffp0gaORbl9//TX+/PNPvH//HtbW1vjjjz8wc+ZMKCsrFzovIyMDu3btwrJly5CTkwMNDQ3MnTtXoKilg6KiIk6dOoWpU6fCxsYGEydOxMSJE9G+fXuJVubKUzM/4HO5mJLKDBDJffr0CcnJycjNzS3zXHl7n5HKMzAwQExMDB49esQn5ZWUlGBgYID79+/j8OHDRXb9ubu7A0CRxExtJJ4oysrKKvbx27dv83/v3r17ic8jnmQSN0CUR6NHj8atW7fg4uKCfv36Ydq0aaWe7+7ujn379oExhrFjx9ZIjNJIfIP65cR3YmIiIiMjwRgrsvNq9OjR2L17t9xPiufk5ODs2bMIDAzEixcvJPotZYzh6tWrNRShdNHQ0EB8fHyh76nGjRvz34NPnjwp9n0IfN7dIK+ysrLg4eGBkydPIiwsDImJiUhPTy91DGNMrsoctW7dGuHh4YUWImhpaUFDQwNJSUm4fv16keTC3bt3AeRfl8g7cRnPL8uPPX/+HG/fvgVjrMh9l/i67vnz5zUSo5Box3LFvHv3DqdOnYK7uzsuXbqE2NhYbNq0CZs2bYKhoSGcnJxgb28PHR0doUMltYz4+qFevXqFjt+7dw+fPn0CYwwjRowo9FiXLl0AAK9fv66ZIKXcyJEjJVooP2HChFJLM0qNKu4pQ6pBnTp1OJFIxPn4+Agdiky4fPkyp6ioyDdKU1NT4wYMGMDZ29tz9vb23IABA7j69evzDa8UFRW5S5cuCR221Lhy5Qqnra1NzejKIG62fPfuXaFDkUmXLl3ixo4dy2lpadH7rATnzp3jjI2NOWNjY87Dw6NcYz08PPixly9frqYIZcOaNWs4xhhnb29f6Pjvv//OMcY4BQUFbsWKFVxERAQXFBTEN2kWiUTcN998I1DUNUfcqLWkxvELFy7kX48LFy6U+DzXrl3jGGOcmppadYUq9VJSUrhmzZrx31kjRozgvLy8uDdv3nBZWVlcdnY29+bNG87Ly4sbMWIEfx3SokULvsGkPFJSUuJEIhF38+bNQsdPnDjBMcY4ZWVlLjMzs9Bjd+/e5RhjnJKSUk2GKlUCAwM5PT29Qr+TXzZc/rKxa0nNwOXFgAEDOJFIxLm6uhY63qFDB04kEnFLly4tMmbOnDn851RePX78mOvUqVOZ77Hi3nPyZObMmZxIJOKWL19e6PiECRM4xhjXrl077v379/zxgs3S+/fvX8PRSh9xc+WAgIBCx11cXDjGGKeurl5kTGhoqNz8FtStW5cTiUTcvXv3in08KiqK/9yNGTOmxOfx9/fnGGNco0aNqilS6RUfH8/9/fffnKmpaaHvqTp16nBDhw7lPDw8uLS0NKHDJLWEmpoaJxKJuCtXrhQ6vnnzZo4xxunq6hYZI/5OU1ZWrqkwSQ2i4ocyQLwSS96bxEtq8ODBuHjxInR0dMBxHFJSUhAQEIDDhw/j8OHDCAgIQGpqKjiOQ/PmzXHp0qVSt/LKk++++w5DhgxBYmIiNaMrg5mZGQAgIiJC4Ehkz4IFCzBs2DCcPn0a79+/p/dZMTiO41enNW7cuNhVbKWxt7eHlpYW7t27hyVLllRTlLJBvLPA29u7UBmyhQsXQk9PD3l5eVizZg26du2KPn36YOfOnQAAdXV1/O9//xMi5BolXknp5eVV5DGO43D69GkAQJ06dfha9cV5+fIlgPzybvJKVVUV3t7efHPg8+fPw87ODrq6ulBWVkbdunWhq6sLOzs7nD9/nq9n7+3tLdc9YDQ0NAB8LkUmJt6l0b179yKrw8Wr6uW199qjR48wbNgwvHr1ChzHQVFREZ07d4a5uTn69+9f7B8LCwv+f+WVmZkZOI5DYGBgoePjxo0Dx3H4559/4OLigtTUVMTHx+Ovv/7Cnj17wBgrtuSFPEhNTcXw4cPx6NEjfrferFmzAOSvpl++fDnmzZuHXr168cf69u2LlStXYsWKFUKGXuOsrKwK/W6KLViwAADw4sULtG/fHnZ2drC2tka3bt343TKzZ8+u8XilTdOmTQEADx8+LHRc3Ki6uGsQ8W42dXX1ao5OeLRjufIaN26MhQsXIjg4GJGRkfjxxx/RokUL5Obm4tKlS3B0dESTJk0wbdo0ud0pSqqOuHx4UFBQoeNnzpwpsVfJhw8fAOS/V0ntQyXIZICRkRHi4uLw5MmTYutjk6IGDhyI58+fw83NDd7e3ggNDeW3AGppacHExASjRo2Cg4NDoeaI8uzAgQP4559/AOTXSrWxsYGRkREaNWpEjeqKsXjxYnh5eWHLli2YPHky6tShr1NJeHp6Ytu2bQAAZWVljB07FqamptDQ0KD3WQE+Pj548uQJFBQUsHnz5nKPZ4zh77//hpGRESIiIuDv719sA0B5YGBgAF9fX+Tk5BQqhaKiogJfX184ODjg+vXrhcZ06dIF7u7uaNGiRU2HW+NGjx6N8+fP8+UZHB0d+cc2bNiA6OhoviluaZPd4lrF8tzLBMjvY3L//n0sXLgQJ0+eLLEclIKCAmxsbLB582Y0b968hqOULkZGRrh8+TI8PT358gHp6ek4evRoiRPf8p7w+/3335GWlgYFBQX8+uuvWLBggdwmo8pj5MiRWLt2LU6ePIlt27bxJYqXLFmCXbt2ISkpCTNnzsTMmTP5MRzHoV69evjpp5+ECltQO3bsQFRUFBQUFHDx4kUMGjQIkZGR2L17NwDg119/5c8NDQ2Fo6Mjbt26hUmTJmH+/PlChS2IkSNHwsLCArm5uXj+/Dnatm0LID9xsGLFCqxevRofPnzA8ePHAYBfWDR9+vRyL7SpjXr37o3o6Gg4OzvDwcEBKioqePHiBd9YubgFk0+ePAHwOXlTm2lqaiI2NhZPnjxBjx49ijx+48YN/u/FPS4m7m/4ZYl2edOpUyf88ccf+OOPP+Dn5wc3Nzd4eXkhOTkZbm5ucHd3R7NmzeDk5IS1a9cKHS6RQQMHDkRkZCS2bt0KGxsbdOrUCadPn+ZLBVpbWxcZI17cK+8l8b5clFVeUlu2vpp32JAq4OXlxTHGuAEDBggdSq0VEhLCfffdd0KHIaiePXtyjDGuU6dOXFxcnNDhyIR///2XU1BQ4EaNGsUlJCQIHY5MsLCw4LfcPnv2TOhwpJa4DNbw4cMr9TziMkfz58+voshqp0ePHnFeXl7c4cOHuZCQEKHDqVGpqamcrq4uX8KoZ8+e3OTJkzljY+NCJYu+3D5fUF5eHl+W8bfffqvB6KVbTEwM5+npyf3888/cnDlzuDlz5nA///wz5+npycXExAgdntQQl5cRiUTcxIkTua1bt3JmZmZ8icCHDx8WGbNkyRKOMcYNGzZMgIiFJy5btGTJEqFDkTmurq7cnj17inwGg4ODuTZt2hQpo9WkSRPu4sWLAkUrvP79+3MikYibPHkyfywiIqLEEmPx8fGcjo4Op6ioyAUHB9dkqFLvypUr3KRJkzhDQ0OuU6dO3OjRo7ljx44JHZbUuHr1Kv++at26NTd+/HiucePGHGOMU1VVLfZea9q0aRxjjLOzsxMg4pplbW3NiUQizsbGpshjeXl5XOvWrflybMnJySU+j4eHB18SjxSWnp7OHTx4kLO2tuYUFRU5xhhXr149ocMiMurJkyd86UCRSMRpamry91YtW7YsUl6X4z7fu8+cOVOAiKVHedohyFLZelqyLQPGjRsHBwcHHDhwADNmzMDWrVuhqqoqdFgyLzY2FgcOHIC7uzsiIyMBoEIrzWsLcWmBVatWye2K0vJYvXo1AKBnz57w9vZGq1atYGVlhY4dO0pUSkbeyjKIhYeHgzGGlStX8isDSVG3b98GYwyjRo2q1POMHDkS586dw61bt6oostqpQ4cOcrtzQ0VFBYcOHcKwYcOQnJyM4OBgBAcHA/i8OnfGjBmwtLQs8TnOnTvHN8gdPHhwjcQtC3R0dGBvby90GFLPyckJ+/btw7Vr13D06FEcPXqUf2z69Ono2LFjkTHHjx/nSx3JI/Gu7i8bUpOyTZ06tdjjpqamePToEXx8fBAZGYmcnBzo6+tj6NChcl0i8MGDBwBKfq/l5eUV2sHcuHFjLF68GD/88AO2bdsGFxeXGolTFlhaWpb6WyrvBg0ahIULF2LLli2Ijo7Gy5cv+euQ9evXQ0tLq9D5GRkZ/O4YeSitSDuWqx9jDCKRCIwxvuQbIRWlr68Pd3d3zJgxA6mpqXx5sUaNGuHgwYNFyuvGxcXh8uXLACC3ZU/FuFpaep5xtfVfVou4ubmB4zhs3rwZ9+/fR6NGjTBq1Ch07doV6urqUFBQKHW8k5NTDUUq/dLT03H8+HG4ubnBx8cHeXl5API/4IyxEkuFyAMtLS0kJSXh7t276Natm9DhSD3xxZmY+D0kKXl9r9WvXx/p6ekIDg6mkoqlaNy4MT58+IDLly9X6gLMx8cHgwcPhqamJhISEqowQlLbPH36FD///DPOnTuH9PR0APm1i7/99lssWrSo1O+33r174/bt29DR0cHbt29rKmRSi6SmpmLlypU4evQo4uLioKOjg6lTp2L58uVFSnx6e3tj9OjRYIzh9u3bMDU1FShq4bRs2RIxMTG4c+cOTExMhA6H1GJKSkrIzc3FrVu3+LJGz549Q/v27cEYw8ePH6GmplZozM2bN9GvXz/o6enhxYsXQoRNZJi3t3eh3wInJ6dir4WPHDmCH374AYwx+Pn58f0Waqu0tDR06tQJb968AZDf56Vdu3Z4+PAhwsLC+HvRS5culZjo4zgOurq6iImJwa+//opffvmlJv8JUsvf3x/u7u7w8vLi+0VyHAcdHR04Ojpi3bp1AkdIZFl8fDzOnj3Lf6eNHj2a739Y0KVLl3Dw4EEAwN9//42GDRvWdKhSY//+/WWek5qaiidPnsDLywtv375Fv379+BKyJS22ERolYGRAZSZ6GWOFat7LK19fX7i5ueH48eNISUkB8DmrqqOjAxsbG4wfPx4DBw4UMkxBmZmZ4ebNmzh79iyGDRsmdDhSr7L9SsTJP3nTpUsXPHz4EH5+fjA3Nxc6HKklnvCobEL03r17MDExgaKiIjIzM6suQFJr5eXlISEhAUpKShI3tRU3cq1Tp47c9FWrbG3i4khtvWIpk5SUxE+Q1PYJt5LY29vjyJEjcHFxoYVW5SCexHV0dMT06dMFjkY2NGrUCMnJyQgKCuIbeycmJkJbWxuMMYSHh8PAwKDQGHECRllZGWlpaUKETUitdPPmTX7H8pfzQwDw1Vdf8f2ZinP27FmMGjUKjDFcv34dvXv3rvaYpdXDhw/h7u4OT09PvH79GkD+66iiogIbGxs4OTnB0tKSepQSIuWys7OxaNEiODs7Y+nSpVKdMKUSZDLiyzwZ5c3K9ujRI7i5ucHDw4NfKSJ+3Vq0aIHx48fD1tYWffv2pS2myC/zcePGDRw8eJASMBKQ1wRKZY0bNw5r167F1atXKQFTigYNGiApKQkfP36s1POIx3+5OlVehYWFITAwEC9evEBycnKZO9EYY9i7d28NRScdRCJRuctQymNZVD09vSq9dqAFM5JTV1eXODlYWy1evBheXl7YsmULJk+eXGSXECleYGAg8vLysHz5cqFDkRmtW7dGeHg4YmJi+GNaWlrQ0NBAUlISrl+/XiQBc/fuXQAoUl5FHn369Emi6w2AkvCkbH369EFwcHCpO5ZL89tvvwEAmjZtKpfJl/j4eBw8eBDu7u4IDQ0FkD8/JBKJMHDgQDg5OWHcuHFyeV1LiKxSVFTEtm3b8PDhQ6xfvx4DBw7E0KFDhQ6rWLQDRga8fPmyUuPlaXXg+/fvcfDgQbi5ufEX/+K3eKNGjfDx40cwxnDw4EFMmDBByFCl0ujRo3H27Fls2bIF8+fPFzocUgv9999/6NatG5KSknDr1q1ia/sToFOnTnjy5Am2bt2Kb775psLP4+zsjHnz5qFjx458HXd59PDhQ3z11VcICgqSeAyVpiSlqeoVkfL8XtuwYQOcnJygra0tdCgyZfv27ViwYAGsra2xb9++Iv0RSFHNmzdHXFwclUEth1mzZmHfvn1YtmwZ3/8QACZOnIijR4+ibdu2CAoK4supvHjxAhYWFoiNjYW5uTn8/PwEilw4ly5dgrOzMwIDA5GUlCTRGErCk/KiHcuSycjIwMmTJ+Hu7o7Lly8jNzeXnx8yMDCAk5MTpkyZgmbNmgkcKant0tPTcffuXcTFxSEtLQ1jx45FgwYNhA6r1jhy5AgmTZoEa2treHt7Cx1OsWi5lAyQpwRKRWRnZ+PMmTNwc3PDhQsXkJ2dzf+oKikpwdraGg4ODhgxYgTq1asncLTSKyAgAAsWLEBCQgIWLlwIT09PTJo0Ce3bt5eo+ag8ND8kldewYUNcvHgRo0aNQt++fbFmzRrY29vL/WrmL/Xs2ROPHz/GmTNnKpWAETcnFddtl0cvXryAmZkZPn78yP82qKmpoVGjRlRWgFRYWbWFP378yH/+qERU6X744QcsW7YM1tbWmDFjBkaMGEGfzTKIJ8J79uwJb29vtGrVClZWVujYsaNE12wrVqyo7hClkpGREeLi4vDkyRNKwEjIysoKe/fuxenTpwslYBYsWICjR4/ixYsXaN++PQYOHIjU1FRcu3YNKSkpYIxh9uzZAkYujDlz5vAloGidK6lOtGNZMtra2nziieM4NG3aFPb29nB0dKS+t6RGvH79Gj///DOOHj2K7Oxs/nj37t3RuXNn/r/37t2LnTt3omHDhrh06RJV6SknfX19AEBwcLDAkZSMdsAQmXXr1i24ubnhyJEj/Ooi8Yrlvn37wsHBARMmTCg0sSvup0M7YIr6stdQedCqLfKlNm3alPp4Wloa4uPjwRgDYwxaWlplThoxxvD8+fOqDFNqHTp0CJMnTwZjDP7+/jAzMyv3cwQEBGDAgAFgjMHDwwOTJk2qhkiln4ODAzw9PSESibBkyRLMnTsXenp6QodFarnIyEgYGhrK9c4WSYmTLeJrEG1tbTg5OWH69Om0S7IElekPCUBu35PHjx+Hra0t+vfvD19fX6HDkQlpaWmwtrZGbm4uXF1d0bZtW/6xVatW8UkZ8ftPPLUwY8YM7Nmzp+YDFtDOnTsxd+5cAPkLPWxsbGBkZCTxgg9pbRpc1RQUFAAUvX8UH68IuhclJRH/XiorK2P06NEYMmRIpd5rAGhhDZFYUFAQRowYgaSkpEJJecYY7t+/XygBEx8fD11dXWRnZ+PcuXNSW0ZLWvn5+WHQoEGoW7cuX55R2lACRgbk5ubizp07CAwMxJMnT5CUlITk5GQ0aNAAGhoa6NChA8zMzNC9e3e5WjEo/jEVv4U7dOgABwcHTJkypcTJNUrAlKwy7x15n2C6evUqXF1dcfPmTcTFxSEjIwPh4eGFflADAgIQERGBBg0awMHBQcBoa0Z1fBfJ0/ssOzsbHTp0QHR0NJo0aYKAgAB+VYcknjx5AgsLCyQkJEBPTw+PHz+W2x4BTZs2RUJCAhYtWoQNGzYIHQ6RE5SAkVxkZCT27t0LDw8PJCQkAPg8mdurVy989dVXmDhxIurXry9kmFKlsr+x8tzHzsnJCQcOHMC0adOwdetWuVwRXhI3NzcAKFdZlKtXr2LPnj2IjIxETk4O9PX14eTkhPHjx1dnqFLJ1NQUoaGh6NSpE3x8fMq9O0FeFEy6F/x9pHtRUh0qs8i0OJTsI5L6+PEjOnbsiPj4eOjo6GD58uUwNzfn7w++TMAAgI2NDU6fPo158+bhn3/+EShy2TR9+nTs378fenp6ePHihdDhFEs+Z2NkRE5ODv79919s2LChUOPDkrRs2RJLly7FnDlzKp3VlyVqamr4559/5GbVUHWhlYDll5aWhqlTp+L48eMAPq/6K+4iT0FBAfPnzwdjDL169SrXZLosos9j5SgqKmLDhg2wtbVFfHw8TE1N8dtvv2HmzJmlThalpKRgz549WLFiBV8CZOPGjXKbfAHyG+ACkMvJIEJkgYGBATZt2oS//voL3t7ecHFxwfnz55GTk4OgoCAEBQXhu+++g62tLaZPn04lTyHfCZTKcHNzg6WlJcLDw7F//36cOnUKo0aNQteuXaGurl7m/VNtX/U8bdo0MMaKlEURS0hIgLOzM4DPZewsLS1haWlZo3FKq0ePHoExhlWrVlHypRQrV64s13FCKovWnBMh/PPPP4iPj4eWlhZu3rwJXV3dMscMHjwYp06dwu3bt2sgwtrh6dOn2LhxI/bv3w/GGKytrYUOqUS0A0ZKvX//HjY2Nrh+/ToAyX40xJO+AwYMwNGjR/lmiLXVlyUrjIyM4ODgAHt7e+jo6JQ4hnbAkKoycuRInD9/HhzHoWfPnrCwsMCGDRtKXNHQtWtXREZGYu3atfjpp58EiprIkt9++w0rV67kv+dUVVVhbm4OU1NTaGtrQ1VVFampqXj37h1CQkIQGBiI1NRU/jdj9erV+OWXX4T8JwiuQ4cOePbsGW7duiXXvXBIzaIdMJUTHx8PNzc3uLq64sGDBwA+X++1bdsW06dPx9SpU6lpLimXypRuk4dVz+LXp7hrWIC+18rSpEkTJCYm4u7du9RbghAp4e/vX+XP2b9//yp/TlL79OjRAyEhIUXmfkr7rRWX0dLU1OR3hcujssrZA/mLkT5+/Ijk5GQA+dd0TZo0QUhISInzwUKjBIwUys3NRb9+/XDnzh3+xsDKygqDBw+GiYkJNDU1Ub9+fSQnJyMxMRGhoaG4fPkyrl69yp/fp08fBAQE1OqSZAEBAXB1dYWXlxf/oWOMQSQSYcCAAXB0dMS4ceMKlaygBAypKl5eXrCzswNjDDt37sTMmTMBlP6DKq6VPXToUJw/f16IsGuUSCSCSCQqUo6NlI+Liwu+/fZbpKWlASh+h5WY+CddRUUF27Ztw7Rp02oiRKm2YMEC/Pvvv/jnn38wb948ocMhcoImKqvO7du3sW/fPhw+fBj//fcfgM/Xe1ZWVvjqq68wevRoKCoqChwpkXZU4qh0lICpnAEDBiAwMBDe3t4YPny40OEQQggRkLq6Oj59+oTAwED07duXP17ab21YWBiMjY2hqKiIzMzMmg5ZalTkeq1Pnz7Yt28fOnToUA0RVQ1KwEihtWvXYvny5WCMoVu3bnB3d5do8jIiIgJOTk64d+8eGGP4/fff8eOPP9ZAxMLKyMjAiRMn4ObmhitXriA3N5efoKxXrx5GjRoFR0dHDB06FIqKipSAIVVi9OjR8Pb2hqOjI/bv388fL+0H9cyZMxgzZgx0dXURHR1dwxHXvLJu5InkYmJisGnTJri5uSExMbHE87S0tDB16lQsWrSIVob/v5cvX/JNcENCQmr97lAiHWiisuplZGTg2LFj+OGHHxAXFwfgc0JaU1MTM2bMwHfffYemTZsKGSaRYi9fvqzU+FatWlVRJNKJEjCV4+npCQcHBzg4OPD9dAghhMinevXqISsrq0gVhtJ+a2/cuAEzMzM0aNAAHz9+rOGIpcf06dPLPEckEkFNTQ2tW7dG//79ZWLnKSVgpEx2djZatGiBxMREGBsb49q1a1BWVpZ4fEZGBvr164fQ0FBoa2vjzZs3clX7Py4uDgcOHMCBAwcQHh4OoPDNeWJiIiVgJHT37l1cuXIFERER+PDhAwBAQ0MDXbp0weDBg2FqaipwhMJq1qwZ3r17hzNnzhSqM1naD2pwcDB69uyJevXqITU1taZDrnGUgKkekZGRCAsLw/v375GcnAw1NTVoamrCyMgIBgYGQocnlXx8fDB27Fg0a9YMW7duhZWVldAhkVqOJiqr1suXL+Hq6or9+/fzk+hf3sIwxqCsrIw1a9Zg0aJFQoRJiEyjBEzl2dnZ4fjx49iyZQvmz58vdDhS69WrV1X+nJL0VyCEkJrSqlUrvHnzpsjcY2m/tdu3b8f8+fPRsWNHvgQvqT3kZ2ZeRpw5cwYJCQkQiUQ4cOBAuZIvAKCsrAx3d3cYGhoiISEB3t7eGDt2bPUEK4WaNm2K77//Ht9//z3CwsKwf/9+HDx4EO/eveOTLwCwePFiXL9+Hba2tjA3Nxc4auly//59zJ49u9TGXz///DN69eqFnTt3wtDQsAajkx7v378HgHLtMhBvpaTmuaQyDAwMKNFSToMGDUJoaCj69u2LYcOGQV1dHe3atYOKikqp4xhjuHr1ag1FSQgpSLzjxcXFBf7+/uA4jk+6tG/fHl999RUcHR0RERGBvXv3wsvLC+np6fj+++/RuHFjODg4CPwvqH5ZWVnw8PDAyZMnERYWhsTERKSnp5c6Rh56mRAiFA8PD3z//ff47rvvcOjQIUyYMAHt27cv83oDACwsLGogQumgp6cncf8lSdD3GiFE2vTq1Qtv3rzB+fPnJVr8zXEcdu/eDcYYzVHWUpSAkTLXrl0DAAwePBgdO3as0HN07twZVlZWuHz5MgIDA+UqAVOQkZERNm3ahPXr1+PixYtwc3PD6dOnkZGRgZiYGGzbtg3btm2DtrY2bGxsMH78eFhaWgodtqCuXLmCUaNGISsri5/kUFRUhKamJoD8pEN2djYA4NatW+jZsye8vb3l8nVr2LAh3r9/j5iYGIm3O0ZFRQHILxNFCKk5N27cgKOjIxITE8FxHD58+FBqkpkxVq7mzISQqnPz5k24uLjgyJEjhRpr1qtXD7a2tpg5c2ahG9OmTZti8ODBeP78OWxtbREWFobNmzfX+gTMkydPMHbsWDx+/LjIbiBCiDAUFRVhZGQEdXV13Lx5Ezdv3pRonDwmEOh7ixBSm02ZMgXHjh2Dh4cHFi5cWOac0ZIlSxAWFgbGGKZOnVozQZIaRQkYKXP37l0wxio9oW1paYlLly7h7t27VRSZ7FJQUIC1tTWsra3x6dMnHD58GO7u7rh+/To4jsO7d++wc+dO7Nq1S+4ufAtKTEyEnZ0dMjMzIRKJ8NVXX2HWrFkwNjbmy9jl5uYiNDQUu3fvxr59+5CZmQk7Ozs8ffqUT9LIi/bt2+PmzZsICwsrVIKsNCdPngQAGBsbV2NkhJCCHjx4gKFDhyItLQ0cx0FZWRn6+vpo1KhRpRoyE/m2evXqUh+Pj4+X+FyxFStWVComWRYbGws3Nze4urriyZMnAD5PzhkbG2PmzJmYMmUKGjRoUOJztG3bFn/++SeGDRvGP0dtlZqaiuHDhyMqKgoikQhjxoxB48aN+ZWTv/zyCz58+IDg4GAEBQWBMYY+ffpQ+UVCqlFOTg7s7e1x/PhxAJRgKE1Zk4sfP37EqVOnwBiDk5NTDUVFCCFVZ8yYMRg4cCB8fX1haWmJNWvWYPz48fzjOTk5iImJwfXr1/HPP//gxo0bYIxh3Lhx6Nu3r4CR16yAgIAqf05p3VFKPWCkTJs2bfDy5UucO3cOQ4cOrfDzXLx4EcOHD0erVq34VfeksOjoaOzfvx8HDhzA8+fP5b6e8fLly7F27VooKSnh1KlTZb7/Ll26hFGjRiEnJwfLli2TeIKptvjjjz+wbNkyNG3aFC9evODLBZZU0zMwMBCDBg1CXl4eduzYgVmzZgkVeo2hHjBEGkycOBFHjx5F3bp1sXHjRsyYMaPc5T0J+ZL4+60qyes1iLW1NS5fvoy8vDx+wrJhw4aYPHkyZs6cWa5FC8+ePUP79u1r/TXdxo0bsXTpUigoKODixYsYNGhQif05QkND4ejoiEePHuHvv/+W674Ubdq0KfcYcW+hhg0bQl9fH71798bEiROhoaFRDREKS/y9NnfuXGhraxd5PD4+Htu3bwdjDCtXrpToOeUpsbxt2zYsWLAAQH7t/6lTp8LIyEjiBR/9+/ev7hBlBvUbIoTUBh8/foSlpSVCQ0PLvG/gOA69e/fG5cuXoaqqWkMRCq+q76mkeUcpJWCkjKamJj5+/IibN2+iZ8+eFX6e27dvo3fv3mjUqBHfQJ2U7Pr163B3d8eOHTuEDkUwJiYmCAsLw6JFi7BhwwaJxnz//ffYtGkTjI2N5W631adPn9CmTRskJSVh+PDhcHNzg4aGRpGkQ05ODlxcXPD9998jJSUFLVu2xNOnT6GoqCj0P6HaiV+L7t27V8lFBPXjIBXRokULxMbGYtWqVVi+fLnQ4ZBaoqp3T8nzJFPB19Lc3BwzZ86EnZ1dhRKlMTExmDx5Mhhj8PX1rcowpcqAAQMQGBiISZMmwcPDA0DpE5YJCQkwMjJCYmIibt68CVNTUyHCFtyXn1txucniFPeYeIKgbt26WL58Of73v/9VT6ACocRy5XTr1g3h4eHo3bs3rl69inr16gkdksyiBAwhpLbIysrCr7/+iu3bt+O///4r9hwVFRXMnz8fq1evhpKSUg1HKCzxtUdVpSak+XeDEjBSRlFREXl5eZVeMS6+aFFQUOB7dhBSGnV1dXz69Am+vr4Sb9kLCAjAgAED0LBhQyQlJVVzhNLn6tWrsLa2Rk5ODpSVldG/f39cuHABjDEMHz4cWVlZCA4Oxn///ceXPvLz86tUclWWVOWNvLgfh7T+mBLppaqqioyMDNy6dQs9evQQOhxSS/j7+1f5c8rr6uemTZti6tSpmDlzJvT19YUORyZoa2vj/fv3OHz4MGxtbQEUnrDMzs4ukmzYsGEDfvjhB0ydOhUuLi5ChC246dOnAwDCw8MRGhoKIH/xW7du3dC4cWMA+cmqe/fu4f3792CMoVu3bujSpQs+ffqEiIgIPH/+HED+Df68efPwzz//CPOPqQaUWK6c+vXrIz09HSdPnsSoUaOEDkemUQKGECJLdu7cCQsLC3Tq1KnEc1JTU+Hv74/g4GDEx8cjNzcXmpqaMDY2xuDBg9GwYcMajFh6iOeMlJWVMWbMGFhZWVX6ekRae+hQDxgpk5ubW6Urj/Ly8qrsuUjtlpGRAQDl2qkgPjczM7NaYpJ2lpaW8PHxgYODA16+fMknXwDg/PnzAD7Xf27ZsiWOHDkiN8mXgijPT4TUokULPHv2jG7gSZWS12RJdXjz5g3fa45I5uPHjwDyyxyJ1a1bl/97amoq1NTUCo3p168fgOpJHsoKFxcXuLm54eDBg2jTpg02b96MESNGFLnRz8vLg7e3NxYtWoTIyEjMmzcPM2bMAADcuXMHs2fPRlhYGP79919MnjwZvXv3FuKfU+Vq866xmqCqqor09HS0bNlS6FAIIYTUoLlz54IxBi0tLZiZmcHCwgIWFhbo1q0bPz+kqqrK96Ymn6mpqSE5ORnp6ek4fPgw/P39MXnyZDg6OqJr165Ch1el6G6HEAIgfwXqq1evEBoaKnFpCvHqwSZNmlRnaFKtX79+ePr0KQ4dOoTTp08Xu6Jh9OjRmDp1qtxtJxWLiIigHjBEMKNGjcLmzZsREBBQaybJCKlNKPlSfioqKkhOTi60aKtRo0b831+9egUDA4Nix8bFxVV3eFIrNDQUs2bNQtOmTXHr1i1oaWkVe55IJMLo0aPRp08fmJqaYu7cuejatSu6d++OHj164MqVK+jatSvi4uKwa9euWvPbQonlyjEyMsLVq1fx8uVLdOvWTehwCCGE1CCO45CQkICTJ0/i5MmTAIAGDRqgb9++fEKmR48edN37hXfv3uHUqVNwd3fHpUuXEBsbi02bNmHTpk0wNDSEk5MT7O3toaOjI3SolUYlyKRMWc0PJVWwSSKt+iWScHJywoEDB9CmTRuEhISgQYMGpZ6fnJwMExMTvHjxAlOmTIGbm1sNRUpkxZf9cAgRwrt379CtWzfk5OTgzp070NPTEzokQuRWQEBAlT+npGVTaxNjY2OEh4fjxIkTGD16NH9cS0sLSUlJcHZ2xuzZswuNETcIb9CgAb+DRt44OjrC09MT//77L+bMmSPRGGdnZ8ybNw+TJk2Cp6cnf3zNmjVYsWIF2rVrhydPnlRXyESGHD9+HLa2thg5ciROnz4tdDgyjUqQEUJkiZubGwIDAxEYGFjkmqDgYhllZWX06tWLT8j06dOH+oUVkJCQAE9PT7i7uyMkJARA/usnEolgaWkJJycn2NjYyOxrRgkYKUM9E4hQrl+/DnNzczDGYGhoiD179qB79+7FnhscHIzZs2fj3r17YIwhICCAL21BiBglYIi0CA8Ph42NDVJSUrBmzRrY2dkVWi1OCKkZVd3kmzGGnJycKns+WTFr1izs27cPy5Ytw+rVq/njEydOxNGjR9G2bVsEBQVBQ0MDAPDixQtYWFggNjYW5ubm8PPzEyhyYbVq1Qpv3rxBUFBQide4XwoODkbPnj3RokULvHr1ij/u5+eHQYMGoX79+vj06VN1hUxkzDfffIMdO3Zg8eLF+PPPP6GgoCB0SDKJEjCEEFmVkJDAJ2MCAwMRFhZW6Hus4HWwoqIiTExM+ISMmZlZmQuh5cXDhw/h5uYGT09PvH79GkD+a6eqqopx48bB0dERlpaWAkdZPpSAkTLU/JAIaf78+fzOKQAwMDBAr169oK2tDcYY3r17h6CgIERGRgLIT/LNmzcPW7duFTJsQfz0009wcHBAly5dhA5FalEChkiDNm3aAADS0tIQHx8Pxhhfo1dFRaXUsYwxvuEyIaTyxL8LVXX7Ia/XuUeOHMGkSZPQtWtX3Lt3jz9ecDGNuro6Bg4ciNTUVFy7dg0pKSlgjMHd3R2TJ08WLngB1atXD1lZWeVaOCR+TevWrYv09HT+eFhYGIyNjaGsrIy0tLTqCpnIEHE1AGdnZ9y+fRstW7bEuHHj0L59+zKvN4D8agQkHyVgCCG1RUpKCm7cuMEnZG7fvs33XwYKJ2REIhEMDQ3Rv39/bN68WYhwpZKfnx/c3Nzg5eWF5ORkAPmvW7NmzeDk5IS1a9cKHKFkKAEjZaqjMSbV8yWS4jgOP/74IzZt2oS8vDwAKLJSVfyVIRKJsGTJEqxbt65KV7PKCvEkkoGBARwcHGBvb09NN79ACRgiDSqzsIFu/AmpWuLfBWVlZYwZMwZWVlaVXnw0derUKopOdqSlpcHa2hq5ublwdXVF27Zt+cdWrVrF74oRX5+Jr91mzJiBPXv21HzAUqJly5aIiYnB8uXLsWrVKonGrFixAmvWrEHz5s35FZhAfjm9AQMGoGXLlnj58mU1RUxkSWV2+Mnrbr6SUAKGEFJbZWdn486dOwgMDERAQABu3LiB//77r9A59N1XvIyMDJw8eRLu7u64fPkycnJyZGohDCVgCCFFREREwNnZGVeuXMHTp08LPaavr4/Bgwdj7ty5cr37Q0FBgZ/QEK+o79evHxwcHGBrawt1dXWBIxQeJWCINJg+fXqlxru4uFRRJISQhg0bFlq51rRpU0yePBmOjo7o2rWrwNFJJ/Gq+rFjx0pcluLq1avYs2cPIiMjkZOTA319fTg5OWH8+PHVGarUE/c7VFVVha+vb5llyO7cuYOBAwciPT29SL9DcU+dnj174tatW9UdOpEBtOBDcgVLJxanYD/blStXSvScK1asqIrQCCGkRmVlZcHV1RV//fUXoqKiqJVEKTIzM3Hq1Cm4ubnh8uXLyM7OpgQMIaT2yMrKQlJSEgBAXV0dSkpKAkckHWJiYnDw4EF4enoiNDQUwOeVpoqKihg2bBimTJmC0aNHo27dukKGKhhKwBBCCCkoIyMDp06dgru7Oy5duoScnBz+t9PQ0BBOTk6wt7eHjo6OwJFKj7J+SxMSEuDs7AyAJiDLEhkZCRMTE+Tk5KBu3bpYsGABHBwcYGBgUGi3UGRkJNzd3bF161ZkZGRASUkJwcHBhRYeWVpaws/PD0uWLMFff/0l1D+JSJHK7oRq1apVFUUi/aq6HxgAmqwkhMiEzMxM3Lp1CwEBAQgMDMStW7eQmpoKAIUW+NJ32mf+/v5wd3eHl5cX33eP4zjo6OjA0dER69atEzhCyVAChhBCKunx48fw8PDAwYMH+X4R4psKNTU1jBs3DpMnT4alpaVclWsT34g2b94cderUETgaQggh0iQhIQGenp5wd3dHSEgIgPzfTpFIBEtLSzg5OcHGxgb16tUTOFJhlZWAoVI95XPgwAFMnz4dubm5/DVZ3bp1oaGhAQD48OEDMjMzAeTf3ItEIri4uMDR0ZF/jufPn2Pw4MEA8ncomZub1/C/ghDZRn1vCSHy4r///sP169f5kmN3795FdnY2gM8JFwUFBRgaGsLMzAxmZmYwNzeX+8VIDx8+hLu7Ozw9PfkSsBzHQUVFBTY2NnBycoKlpWWV/55UJ0rAECKnzp8/j2XLlgEAvv/++3I1ZPX09MSGDRsAAH/99Rd/E0qA27dvw8PDA0eOHMG7d+8AfE7GNGnSBPb29pg8eTJMTU2FDJMQQgiRGg8fPoSbm1uhmyzGGFRVVTFu3Dg4OjrC0tJS4CiFQQmYqnfjxg3MmzcPYWFhpZ7XtWtX/Pvvv+jXr18NRUaIfKC+t4SQ2iouLg6BgYF8wiUiIoJPtIj/V0VFBT179uQTLn369IGampqQYUuF+Ph4HDx4EO7u7nyVGfFimIEDB8LJyQnjxo2DqqqqwJFWDCVgCJFDHMehU6dOePr0KQYPHoyLFy+We/zQoUNx5coVGBoalnkDK4/y8vJw9epVHDhwACdPnixU854abRIirHfv3iEiIgIfPnwAAGhoaKBLly5o0qSJwJERQvz8/ODm5gYvL69Cv53NmjWDk5MT1q5dK3CENYsSMNXn9u3buHLlCiIiIgqV2zUwMIClpSV69+4tcISEEEIIkXaurq580kVcEQX4nHDR0tJCv379+N0tJiYmVCHk/2VkZODkyZNwd3fH5cuXkZuby79uBgYGcHJywpQpU9CsWTOBI608SsAQIoeuXr0KKysrKCgoICwsrEL9OR48eAAjIyPk5eXBx8eHVh2VIjMzE56enliyZAk+fvxIkySECIDjOOzatQvbtm3DgwcPij2nc+fO+PbbbzFr1iy5KhdIiDT68oYsJydHphptVhVKwBBCCCGESC/xtZp4er1t27b87hYzMzN06NBB4AilV4MGDQr1wGnatCns7e3h6OiIbt26CRtcFaOUGyFyyMvLCwBgZWVV4ebonTt3xtChQ3H+/HkcO3aMEjDFyM3Nxfnz5+Hh4YEzZ84gPT1d6JAIkUtJSUkYPXo0bty4AeDzaqQvPXjwAHPnzoW7uzvOnDmDRo0a1WCUhJCCxP1gxDtHCSFEGgwaNAhA/nfU1atXixyviC+fixBCiOypU6cO7OzsYGtrCzMzMzRu3FjokKReSkoKGGNQVlbG6NGjMWTIECgoKCA8PBzh4eEVek4nJ6cqjrJqUAKGEDl0+/ZtMMYwatSoSj3PyJEjce7cOdy6dauKIqsdAgMD4enpiWPHjvEljsQTvi1btoS9vb2Q4REiVziOw5gxY3D9+nUAgKamJiZMmIBevXqhadOmAPJr9d6+fRtHjhxBYmIibty4gTFjxlRLjXJCSOn8/f3h7u4OLy8vfPr0CUD+51hHR6dQI3RCqgLHcXjx4kWhkpRt2rShpB8pkZ+fHwAUeY/4+fkVWgEtCfH59H4jhBDZpa6ujqSkJOTk5ODQoUM4dOgQAEBfX58vO2ZmZoa2bdsKHKn0ysjIwJEjR3DkyJFKPQ9jjBIwhBDp8fLlSwCo9FbI9u3bAwCio6MrG5LMu3//Pjw9PXHw4EG+gbD4BkxdXR22traYMmUKLCwshAyTELnj6emJa9eugTGGyZMnY/v27cU2OXRycsK6deswb948uLu749q1azh48CAlTAmpAQ8fPoS7uzs8PT0L/YaqqKjAxsYGTk5OsLS0hEgkEjhSUltcvHgR27Ztg5+fX5GydioqKhg4cCDmz5+PIUOGCBQhkVYWFhbFJkxKOk4IIaR2e//+PSIjIxEYGIiAgAAEBgbi7du3ePLkCZ48eQIXFxcAQJMmTQqVJjM2NqbfDZRcnaK2oR4whMghJSUl5Obm4u7du5Wqq3jv3j2YmJhAUVERmZmZVRegjHj16hU8PT3h6emJyMhIAJ9/PJSVlTFy5EhMmTIF1tbWUFRUFDJUQuTWiBEjcP78eQwYMAA+Pj4SjRk4cCD8/f0xfPhwnD17tpojJEQ+xcfH4+DBg3B3d0doaCiA/N9QkUiEgQMHwsnJCePGjYOqqqrAkQpLXIZt7ty50NbWLvJ4fHw8tm/fDsYYVq5cKdFzrlixoqrDlBlZWVmYNm0aDh8+DKDkm37xhMjEiRPh6uoKJSWlGouREEIIIbItOjqaT8YEBgbiyZMn/GPia4z69eujd+/e/C6Z3r17Q1lZWaiQBVEdFSektT0CJWAIkUNaWlpISkrC1atXMWDAgAo/j5+fHwYNGgQNDQ0kJiZWXYAywNzcHDdv3gTHcfzNu3jSaMqUKRg/fnyxq+wJITVLR0cH8fHxOHr0KMaNGyfRmOPHj8PW1hZNmzZFTExMNUdIiPzIyMjAyZMn4e7ujsuXLyM3N5f/DTUwMICTkxOmTJmCZs2aCRyp9BAnYKpSbm5ulT6fLLG1tcWJEyfAcRzq1KkDKyurYktSXr58GdnZ2WCMYfz48ZUuiUEIIYQQ+ZWQkFAoIRMWFoa8vDwAnxMyderUgbGxMczNzbF+/XohwyXVgBIwhMihTp064cmTJ9i6dSu++eabCj+Ps7Mz5s2bh44dO+LBgwdVGKH0K1gGxcTEBFOmTMGkSZOgo6MjYFSEkC/VrVsXOTk5CA4OhrGxsURjQkNDYWpqCiUlJWRkZFRzhITIjwYNGiA1NRVA/s6Dpk2bwt7eHo6OjpXakVubVXXZNcaY3CZgzp49i1GjRoExhgEDBmDfvn1o1apVsee+evUKM2bMgI+PDxhjOHPmDKytrWs4YkIIIYTURsnJybh+/Tpftiw4OJivKiPP12q1GfWAIUQO9ezZE48fP8aZM2cqlYA5deoUGGPo0aNHFUYnG9q0aYPJkydjypQple6lQwipPg0bNsT79+8RExMjcQImNjYWQP5kMSGk6qSkpIAxBmVlZYwePRpDhgyBgoICwsPDER4eXqHnlNZGm1XF19dX6BBqDVdXVwCAkZERLly4UGp5WF1dXZw/fx69evVCWFgYXFxcKAFDCCGEkCqhpqaGNm3a4O3bt3j9+jWio6MRExMjN/1Q5BHtgCFEDh06dAiTJ08GYwz+/v4wMzMr93MEBARgwIABYIzBw8MDkyZNqoZICSGkcgYNGgR/f3/Y2Njg2LFjEo2xs7ODl5dXufrGEELKVtXltBhjyMnJqbLnI7Vby5YtERMTAzc3N0yZMkWiMZ6ennBwcEDz5s3x+vXrao6Q1CbJycmIiopCcnKyRCuZLSwsaiAqQgghQuA4Dvfu3eNLkAUGBiIhIaHIOQDtgKmtaAcMIXJo/Pjx0NPTQ3R0NOzs7BAQEAB9fX2Jxz958gQTJkwAYwx6enqwtbWtxmgJIaTibG1t4efnhxMnTmDVqlVYuXJlqRPAv/32G7y8vMAYg52dXQ1GSoh8oLVfRCjiiY7OnTtLPKZjx44AIHe9DknF7d69G9u3b8f9+/cl/r6jZDIhhNQu2dnZCAoK4pMtN27cQHJyMv/4l78Pbdu2hbm5OSwsLCghX0vRDhhC5JS4yTRjDKqqqvjtt98wc+ZMqKqqljgmJSUFe/bswYoVK/gyIl5eXhg7dmzNBU4IIeWQnZ2Nrl274vHjx2CMwcDAANOmTUOvXr2gra0NxhjevXuHoKAg7N+/HxEREeA4Dp06dUJYWBjq1KG1KoRUFX9//yp/zv79+1f5c5LaSVNTEx8/fsTFixcxePBgicZcvXoVVlZWUFdXx/v376s5QiLLcnNzMX78eJw5cwZA+ZLNtNqZEEJkW0pKCm7cuIGAgAAEBgbizp07fE8XoPBvgvie1MLCgk+6UC/h2o8SMITIsd9++63QanBVVVWYm5vD1NQU2traUFVVRWpqKt69e4eQkBAEBgYiNTWV//FYvXo1fvnlFyH/CdVuxowZVf6cjDHs3bu3yp+XEFK86OhoWFpaIioqqszyRxzHoU2bNvDx8YGurm4NRUgIIaS69e3bF0FBQZgxYwZ2794t0ZjZs2djz5496N27N27cuFHNERJZ9u+//+Lbb78FADRp0gTTp0+HqakpNDQ0IBKJyhxPyWRCCJFN3bt3R1hYGPLy8vhjBafa69SpA2NjYz7hYm5uDnV1dSFCJQKiBAwhcs7FxQXffvst0tLSAKDUyUnx14WKigq2bduGadOm1USIgqrqevUcx9EqN0IEkJqailWrVmHv3r34+PFjsec0atQIM2fOxIoVK1C/fv2aDZAQQki1WrduHX7++WeIRCLs2bOnzOtYd3d3TJ8+HRzH4Y8//sAPP/xQM4ESmdSrVy/cuXMHnTt3RmBgIE2uEUKInPgyya6srIyePXvy5cT69OlTaqUZIh8oAUMIQUxMDDZt2gQ3N7dSa1xraWlh6tSpWLRoEZo1a1aDEQpHT0+v1ARMWlpaoeZpSkpK0NDQAAB8+PABWVlZAPITW1paWlBRUQEAREVFVWPUhJCSZGVl4e7du4iIiMCHDx8AABoaGujSpQtMTU2hpKTEn3v37l2YmpoKFSohhJAqlJqaivbt2yMuLg4AMHz4cMyYMaPYkpT79u3D+fPnwXEcmjdvjsePH/PXcIQUp0GDBkhNTYWnpycmTpwodDiEEEJqSIMGDdCvXz8+4dKjR49C95SEAJSAIYR8ITIyEmFhYXj//j2Sk5OhpqYGTU1NGBkZwcDAQOjwpMrdu3dhZ2eHN2/eYNasWZgxYwa6desGBQUFAPm1oMPCwrB3717s3r0bLVq0wNGjR2lClxApd+PGDfz222+4fPkyNcUlhJBaJDQ0FIMHD0ZSUpJEJSnV1dXh4+MDIyOjGoqQyCpxAubu3bvo1q2b0OEQQgipIXl5eRKVmiTyjRIwhBBSATExMTA2NsanT59w7tw5DBw4sNTz/f39MWzYMDRs2BChoaHUZI0QKXT16lWsWbMGAQEB/DEqF0gIIbVLTEwMFi5ciJMnT5b4Ha+goAAbGxts3rwZzZs3r+EIiSwyNTXFvXv3cPnyZQwaNEjocAghhBAiReoIHQAhhMiijRs3IiEhAT/++GOZyRcgv7Hmd999hz///BPr16/Hpk2baiBKQuQTx3E4ceIErly5gtevX0NRURF6enqwtbVF3759i5zv5+eHn3/+GUFBQfx4ABgyZEiNxk0IIaT6NWvWDEePHkVsbCz8/PyKLUk5YMAAWixDymXSpEkIDQ2Ft7c3JWAIIYQQUgjtgCGEkAro0KEDnj17Bn9/f5iZmUk05tq1a7CwsIC+vj4eP35czRESIp9evnyJMWPG4P79+8U+bmdnBw8PDygoKOD9+/eYOXMmTp8+DSA/8cIYw+jRo7Fs2TJ07969JkMnhBBCiIzKzMxE79698ejRI1y6dAnm5uZCh0QIIYQQKUFF6gghpALevHkDAFBWVpZ4jPhc8VhCSNXKysrCyJEjER4eDo7jiv1z9OhR/O9//8Pr16/Ro0cPnD59GhzHQSQSwd7eHuHh4Thx4gQlXwghhBAisbp16+LixYswNTWFlZUVfvjhB9y7dw8ZGRlCh0YIIYQQgVEJMkIIqQBVVVVkZGTgzp07Ek/U3r59GwCgoqJSnaERIrc8PDwQGRkJxhhatWqFX375BYaGhlBSUsLDhw+xfv16hIaGwtnZGTdv3kR0dDQAYPz48fj999+hr68v7D+AEEIIITJJQUGB/zvHcdi4cSM2btwo0VjGGHJycqorNEIIIYQIjEqQEUJIBYwYMQLnz59H8+bNERISgsaNG5d6fnx8PExNTRETE4OhQ4fi3LlzNRQpIfJj1KhROHv2LFq2bInIyEjUr1+/0ON5eXmwsLDAjRs3AORPluzduxdOTk5ChEsIIaQaBAQE8H+3sLAo9nhFFHwuQr4kElW8uAhjDLm5uVUYDSGEEEKkCe2AIYSQCpg/fz7Onz+PmJgY9OrVC5s2bcLo0aOL3Hzl5eXhzJkzWLx4Md6+fQvGGBYsWCBQ1ITUbmFhYWCMYenSpUWSL0D+5Mjq1asxePBgMMbg6OhIyRdCCKllBgwYAMZYkV0F4uMVQTsUSFlWrlwpdAiEEEIIkVK0A4YQQipo8eLF+Pvvv/mbeXV1dRgbG0NbWxuMMbx79w737t3Dhw8fIP6qXbhwITZv3ixk2ITUWuLSgFevXsWAAQOKPScxMZH/jJ45cwbW1tY1GyQhhJBqJV4M8+WuAtqhQAghhBBChEA7YAghpII2bdoEXV1d/PLLL0hLS8OHDx/g4+NT6Bxx4qVevXr47bffsHjxYiFCJUQupKengzEGbW3tEs/R0tLi/96iRYuaCIsQQkgN8vX1LddxQoQUGhoKNzc3WqBFCCGE1GK0A4YQQiopISEB+/fvx5UrV3D//n18+PABQP6OGENDQwwePBhTp04tdVKYEFJ5IpEIjDHcv38fnTt3rvR5hBBCCCFVLTY2FgcOHIC7uzsiIyMBgHZYEUIIIbUYJWAIIaQG0So3QqoPJWAIIYQQIo3S09Nx/PhxuLm5wcfHB3l5eQDyd8tTiTtCCCGkdqMSZIQQUs2KW+VGCRhCqs/27dsl2nEmyXkrVqyoqrAIIYRIuaSkJOTk5EBLS4vv8UdIZfj6+sLNzQ3Hjx9HSkoKgM8linV0dGBjY4Px48cLGSIhhBBCqhntgCGEkGpAq9wIqXninS1ViT6rhBAi23JycvDo0SNkZmZCV1cXjRs3LvR4RkYGfv/9d+zbtw+xsbEA8nv3DRs2DL/++isMDAyECJvIsEePHsHNzQ0eHh548+YNgM9JlxYtWmD8+PGwtbVF3759KdFHCCGEyAFKwBBCSBWSdJXbwIEDhQyTkFpJJBJV6fNRspQQQmQXx3FYuXIl/vnnHyQnJ/PH+/Tpg82bN6NHjx7IysqClZUVrl27xo8RY4xBWVkZp0+fhqWlZY3HT2TL+/fvcfDgQbi5ueHu3bsAPr+fGjVqhI8fP4IxhoMHD2LChAlChkoIIYSQGkYlyAghpJJolRsh0sHX11foEAghhEiJ6dOnw93dHUDhxMqNGzcwbNgwBAUFYfv27QgMDAQAaGhoQF9fHzk5OXjw4AHS09ORnp6OKVOm4PHjx2jYsKEg/w4ivbKzs3HmzBm4ubnhwoULyM7O5t9rSkpKsLa2hoODA0aMGIF69eoJHC0hhBBChEIJGEIIqQBJV7lt2LCBVrkRUkP69+8vdAiEEEKkgHhHMmMMdevWxYgRI9C6dWu8fPkS3t7e+PjxIzZv3oyDBw9CUVER//77L7766it+oUx6ejpWrlyJDRs2ICEhAa6urli4cKHA/yoiLW7dugU3NzccOXIESUlJAD6XGe7Xrx8cHBwwYcIEqKurCxwpIYQQQqQBJWAIIURCtMqNEEIIIUT6ubi4AAC0tbXh4+ODTp068Y89evQIgwYNwq5du5CXl4elS5di5syZhcbXq1cPf/31F+7fv4+LFy/i7NmzlIAhPPGudvF9QIcOHeDg4IApU6ZAT09P2OAIIYQQInUoAUMIIWWgVW6EEEIIIbIjKCgIjDEsWrSoUPIFADp27IhFixbhxx9/BGMMjo6OJT7P1KlTcfHiRURGRlZ3yEQGqamp4Z9//sHUqVOFDoUQQgghUowSMIQQUgZa5UYIIYQQIjtiYmIAAH369Cn28YLH27VrV+Lz6OvrAwA+fPhQhdGR2oDjOKSkpGDGjBnYsmULHBwcYG9vDx0dHaFDI4QQQoiUEQkdACGEyAo1NTW4uLjg4cOHWLZsGSVfCCGEEEKkUGpqKgBAQ0Oj2McbNWrE/71u3bolPo+ysjIAICsrq+qCIzLPz88P06ZNQ/369cFxHO7du4elS5dCV1cXVlZWcHNzQ0pKitBhEkIIIURKUAKGEEIkUHCVm4mJCTZt2oTY2FihwyKEEEIIISVgjJXrOCGSsLCwwL59+/Du3Tt4eHhg6NChEIlEyM3NhY+PD6ZPn46mTZvC3t4e586dQ25urtAhE0IIIURAlIAhhJAy0Co3QgghhBBCSEHKysqwt7fH+fPn8fr1a/z1118wNDQEx3FIS0vDkSNHMGrUKCpLRgghhMg5SsAQQkgZaJUbIYQQQgghpCRNmzbF999/j3v37iE0NBTfffcdtLW1wXEcEhMT+V1XixcvxsKFCxEYGChwxIQQQgipKYwTd5UmhBAisbi4OBw4cAAHDhxAeHg4gM/lLDQ1NfkbrYMHD2LChAlChkoIIYQQIldEIhEYY5g7dy60tbWLPB4fH4/t27eDMYaVK1eW+DwFz6MFNqS8cnNzcfHiRbi5ueH06dPIyMgA8PmeQVtbGzY2Nhg/fjwsLS2FDJUQQggh1YgSMIQQUklhYWHYv38/Dh48iHfv3gH4fGOlo6OD8ePHw9bWFubm5kKGSQghhBAiF8QJmKrAcRwlYEilffr0CYcPH4a7uzuuX78O8TQMYwyMMeTk5AgcISGEEEKqCyVgCCGkitAqN0IIIYQQ4YlEVVtpmxIwpCpFR0dj//79OHDgAJ4/f07vL0IIIaSWowQMIYRUA1rlRgghhBAiDH9//yp/zv79+1f5cxJy/fp1uLu7Y8eOHUKHQgghhJBqQgkYQgipZrTKjRBCCCGEEEIIIYQQ+UMJGEIIqUG0yo0QQgghhBBCCCGEEPlACRhCCCGEEEIIIYQQQgghhJAqVrXdCQkhhBBCCCGEEEIIIYQQQgglYAghhBBCCCGEEEIIIYQQQqoaJWAIIYQQQgghhBBCCCGEEEKqGCVgCCGEEEIIIYQQQgghhBBCqhglYAghhBBCCCGEEEIIIYQQQqoYJWAIIYQQQgghhBBCCCGEEEKqGCVgCCGEEEIIIYQQQgghhBBCqhglYAghhBBCCCGEEEIIIYQQQqrY/wGIk61m3/EHVwAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 进行字符串分割\n",
    "temp_list = [i.split(\",\") for i in df[\"Genre\"]]\n",
    "# 获取电影的分类\n",
    "genre_list = np.unique([i for j in temp_list for i in j]) \n",
    "\n",
    "# 增加新的列\n",
    "temp_df = pd.DataFrame(np.zeros([df.shape[0],genre_list.shape[0]]),columns=genre_list)\n",
    "for i in range(1000):\n",
    "    # temp_list[i] ['Action','Adventure','Animation']\n",
    "    # temp_df.ix[i,temp_list[i]]=1\n",
    "    # 使用loc\\iloc方法进行创建\n",
    "    temp_df.loc[i, temp_list[i]] = 1\n",
    "    temp_df.iloc[i, temp_df.columns.get_indexer(temp_list[i])] = 1\n",
    "\n",
    "print(temp_df.sum().sort_values())\n",
    "temp_df.sum().sort_values(ascending=False).plot(kind=\"bar\",figsize=(20,8),fontsize=20,colormap=\"cool\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-23T02:53:33.348013600Z",
     "start_time": "2024-02-23T02:53:32.844664200Z"
    }
   },
   "id": "313d1b79dd8bdc9b",
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "23e3e03c4648fb31"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
